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Stephan Juricke, Tim N. Palmer, and Laure Zanna


In global ocean models, the representation of small-scale, high-frequency processes considerably influences the large-scale oceanic circulation and its low-frequency variability. This study investigates the impact of stochastic perturbation schemes based on three different subgrid-scale parameterizations in multidecadal ocean-only simulations with the ocean model NEMO at 1° resolution. The three parameterizations are an enhanced vertical diffusion scheme for unstable stratification, the Gent–McWilliams (GM) scheme, and a turbulent kinetic energy mixing scheme, all commonly used in state-of-the-art ocean models. The focus here is on changes in interannual variability caused by the comparatively high-frequency stochastic perturbations with subseasonal decorrelation time scales. These perturbations lead to significant improvements in the representation of low-frequency variability in the ocean, with the stochastic GM scheme showing the strongest impact. Interannual variability of the Southern Ocean eddy and Eulerian streamfunctions is increased by an order of magnitude and by 20%, respectively. Interannual sea surface height variability is increased by about 20%–25% as well, especially in the Southern Ocean and in the Kuroshio region, consistent with a strong underestimation of interannual variability in the model when compared to reanalysis and altimetry observations. These results suggest that enhancing subgrid-scale variability in ocean models can improve model variability and potentially its response to forcing on much longer time scales, while also providing an estimate of model uncertainty.

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


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.

Significance Statement

Weather and climate models provide vital information for decision-making, and will become ever more important in the future with a changed climate and more extreme weather. A central limitation to improved models are computational resources, which is why some weather forecasters have recently shifted from conventional 64-bit to more efficient 32-bit computations, which can provide equally accurate forecasts. Climate models, however, still compute in 64 bits, and adapting to lower precision requires a detailed analysis of rounding errors. We develop methods to quantify rounding error in a climate model, and find similar precision acceptable across weather and climate models, with even 16 bits often sufficient for an accurate climate. This opens a promising avenue for computational efficiency gains in climate modeling.

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Susanna Corti, Tim Palmer, Magdalena Balmaseda, Antje Weisheimer, Sybren Drijfhout, Nick Dunstone, Wilco Hazeleger, Jürgen Kröger, Holger Pohlmann, Doug Smith, Jin-Song von Storch, and Bert Wouters


The impact of initial conditions relative to external forcings in decadal integrations from an ensemble of state-of-the-art prediction models has been assessed using specifically designed sensitivity experiments (SWAP experiments). They consist of two sets of 10-yr-long ensemble hindcasts for two initial dates in 1965 and 1995 using either the external forcings from the “correct” decades or swapping the forcings between the two decades. By comparing the two sets of integrations, the impact of external forcing versus initial conditions on the predictability over multiannual time scales was estimated as the function of lead time of the hindcast. It was found that over time scales longer than about 1 yr, the predictability of sea surface temperatures (SSTs) on a global scale arises mainly from the external forcing. However, the correct initialization has a longer impact on SST predictability over specific regions such as the North Atlantic, the northwestern Pacific, and the Southern Ocean. The impact of initialization is even longer and extends to wider regions when below-surface ocean variables are considered. For the western and eastern tropical Atlantic, the impact of initialization for the 700-m heat content (HTC700) extends to as much as 9 years for some of the models considered. In all models the impact of initial conditions on the predictability of the Atlantic meridional overturning circulation (AMOC) is dominant for the first 5 years.

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