08 Nov 2023
 | 08 Nov 2023
Status: this preprint is open for discussion.

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

Hugo Banderier, Christian Zeman, David Leutwyler, Stefan Rüdisühli, and Christoph Schär


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.

Hugo Banderier et al.

Status: open (until 03 Jan 2024)

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Hugo Banderier et al.

Hugo Banderier et al.


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Short summary
We investigate the effects of reduced precision arithmetic in a state-of-the-art regional climate model by studying the results of 10-year-long simulations. After this time, the results of the reduced precision and the standard implementation are hardly different. This should encourage the use of reduced precision in climate models to exploit the speedup and memory savings it brings. The methodology used in this work can help researchers verify reduced precision implementations of their model.