The reliability of single precision computations in the simulation of deep soil heat diffusion in a land surface model

Harvey, Richard; Verseghy, Diana

doi:10.1007/s00382-015-2809-5

Cited by 11 publications

(24 citation statements)

References 38 publications

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“…The worst-case scenario for precision loss is if one of the numbers is much smaller than the other, when the loss of precision can be total, meaning all the non-zero bits in the smaller number's significand are lost and the result of the addition will simply be the larger number. Harvey and Verseghy (2016) identify this as the reason their model fails to represent deep soil temperatures when using 32-bit arithmetic. Harvey and Verseghy (2016) propose that the climate and weather modelling community avoid this potential problem by always using double, or even higher precision.…”

Section: Low Precision For Land Surface Modelsmentioning

confidence: 99%

“…Harvey and Verseghy (2016) identify this as the reason their model fails to represent deep soil temperatures when using 32-bit arithmetic. Harvey and Verseghy (2016) propose that the climate and weather modelling community avoid this potential problem by always using double, or even higher precision. They mention that the use of extended precision (128-bit, and even 256-bit arithmetic) is becoming more common for computations in other scientific disciplines, and that climate modelling should also be following this trend towards higher precision.…”

Section: Low Precision For Land Surface Modelsmentioning

confidence: 99%

“…An example of such a problem is given in Harvey and Verseghy (2016), where deep soil processes in a climate simulation are disrupted in the CLASS model (Verseghy 2000) when it is switched from 64-bit double precision to 32-bit single precision. Their key finding is that 32-bit precision is not sufficient to resolve small changes in temperature in a deep soil column over century time-scales.…”

Section: Low Precision For Land Surface Modelsmentioning

confidence: 99%

“…This paper presents an alternative view of the problem discussed in Harvey and Verseghy, in which we can have both an accurate simulation, and save computational resources by using lower precision. Section 2 discusses the problems described in Harvey and Verseghy (2016), Sect. 3 demonstrates the application of low precision in an idealised land-surface representation, Sect.…”

Section: Introductionmentioning

confidence: 99%

“…As is common in weather and climate modelling, these schemes are often computed in high precision, even though the actual information content in the variables being computed may be lower. Another reason to choose land surface schemes as a focus is the recent work of Harvey and Verseghy (2016), who studied the effect of single precision arithmetic on deep soil climate simulations. They found that single precision led to large errors in longterm simulations of deep soil temperature, and rightly raised concerns about the impact of reduced precision in climate models, but in doing so dismissed the idea that the weather and climate community should consider moving models to lower precision outright.…”

Section: Introductionmentioning

confidence: 99%

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Reliable low precision simulations in land surface models

et al. 2017

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Weather and climate models must continue to increase in both resolution and complexity in order that forecasts become more accurate and reliable. Moving to lower numerical precision may be an essential tool for coping with the demand for ever increasing model complexity in addition to increasing computing resources. However, there have been some concerns in the weather and climate modelling community over the suitability of lower precision for climate models, particularly for representing processes that change very slowly over long time-scales. These processes are difficult to represent using low precision due to time increments being systematically rounded to zero. Idealised simulations are used to demonstrate that a model of deep soil heat diffusion that fails when run in single precision can be modified to work correctly using low precision, by splitting up the model into a small higher precision part and a low precision part. This strategy retains the computational benefits of reduced precision whilst preserving accuracy. This same technique is also applied to a full complexity land surface model, resulting in rounding errors that are significantly smaller than initial condition and parameter uncertainties. Although lower precision will present some problems for the weather and climate modelling community, many of the problems can likely be overcome using a straightforward and physically motivated application of reduced precision.

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Section: Low Precision For Land Surface Modelsmentioning

confidence: 99%

Section: Low Precision For Land Surface Modelsmentioning

confidence: 99%

Section: Low Precision For Land Surface Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reliable low precision simulations in land surface models

et al. 2017

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Modeling Round-Off Errors in Hydrodynamic Simulations

Weens

Vazquez-Gonzalez

Salem-Knapp

2022

Lecture Notes in Computer Science

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The growth of the computing capacities makes it possible to obtain more and more precise simulation results. These results are often calculated in binary64 with the idea that round-off errors are not significant. However, exascale is pushing back the known limits and the problems of accumulating round-off errors could come back and require increasing further the precision. But working with extended precision, regardless of the method used, has a significant cost in memory, computation time and energy and would not allow to use the full performance of HPC computers. It is therefore important to measure the robustness of the binary64 by anticipating the future computing resources in order to ensure its durability in numerical simulations. For this purpose, numerical experiments have been performed and are presented in this article. Those were performed with weak floats which were specifically designed to conduct an empirical study of round-off errors in hydrodynamic simulations and to build an error model that extracts the part due to round-off error in the results. This model confirms that errors remain dominated by the scheme errors in our numerical experiments.

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