Climate Modeling in Low Precision: Effects of Both Deterministic and Stochastic Rounding

Abstract

Motivated by recent advances in operational weather forecasting, we study the efficacy of low-precision arithmetic for climate simulations. We develop a framework to measure rounding error in a climate model, which provides a stress test for a low-precision version of the model, and we apply our method to a variety of models including the Lorenz system, a shallow water approximation for flow over a ridge, and a coarse-resolution spectral global atmospheric model with simplified parameterizations (SPEEDY). Although double precision [52 significant bits (sbits)] is standard across operational climate models, in our experiments we find that single precision (23 sbits) is more than enough and that as low as half precision (10 sbits) is often sufficient. For example, SPEEDY can be run with 12 sbits across the code with negligible rounding error, and with 10 sbits if minor errors are accepted, amounting to less than 0.1 mm (6 h)−1 for average gridpoint precipitation, for example. Our test is based on the Wasserstein metric and this provides stringent nonparametric bounds on rounding error accounting for annual means as well as extreme weather events. In addition, by testing models using both round-to-nearest (RN) and stochastic rounding (SR) we find that SR can mitigate rounding error across a range of applications, and thus our results also provide some evidence that SR could be relevant to next-generation climate models. Further research is needed to test if our results can be generalized to higher resolutions and alternative numerical schemes. However, the results open a promising avenue toward the use of low-precision hardware for improved climate modeling.

Document Details

Document Type
Pub Defense Publication
Publication Date
Feb 15, 2022
Source ID
10.1175/jcli-d-21-0343.1

Entities

People

  • E. Adam Paxton
  • Leo Saffin
  • Matthew Chantry
  • Milan Klöwer
  • Tim Palmer

Organizations

  • Natural Environment Research Council
  • Office of Naval Research Global
  • Royal Society
  • University of Leeds
  • University of Oxford

Tags

Readers

  • Computational Modeling and Simulation
  • Linear Algebra
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers