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

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

Quantifying aviation's contribution to global warming

Growth in aviation contributes more to global warming than is generally appreciated because of the mix of climate pollutants it generates. Here, we model the CO2 and non-CO2 effects like nitrogen oxide emissions and contrail formation to analyse aviation's total warming footprint. Aviation contributed approximately 4% to observed human-induced global warming to date, despite being responsible for only 2.4% of global annual emissions of CO2. Aviation is projected to cause a total of about 0.1 °C of warming by 2050, half of it to date and the other half over the next three decades, should aviation's pre-COVID growth resume. The industry would then contribute a 6%-17% share to the remaining 0.3 °C-0.8 °C to not exceed 1.5 °C-2 °C of global warming. Under this scenario, the reduction due to COVID-19 to date is small and is projected to only delay aviation's warming contribution by about five years. But the leveraging impact of growth also represents an opportunity: aviation's contribution to further warming would be immediately halted by either a sustained annual 2.5% decrease in air traffic under the existing fuel mix, or a transition to a 90% carbon-neutral fuel mix by 2050

Earth Virtualization Engines -- A Technical Perspective

Participants of the Berlin Summit on Earth Virtualization Engines (EVEs) discussed ideas and concepts to improve our ability to cope with climate change. EVEs aim to provide interactive and accessible climate simulations and data for a wide range of users. They combine high-resolution physics-based models with machine learning techniques to improve the fidelity, efficiency, and interpretability of climate projections. At their core, EVEs offer a federated data layer that enables simple and fast access to exabyte-sized climate data through simple interfaces. In this article, we summarize the technical challenges and opportunities for developing EVEs, and argue that they are essential for addressing the consequences of climate change

Earth Virtualization Engines: a technical perspective

Participants of the Berlin Summit on Earth Virtualization Engines (EVEs) discussed ideas and concepts to improve our ability to cope with climate change. EVEs aim to provide interactive and accessible climate simulations and data for a wide range of users. They combine high-resolution physics-based models with machine learning techniques to improve the fidelity, efficiency, and interpretability of climate projections. At their core, EVEs offer a federated data layer that enables simple and fast access to exabyte-sized climate data through simple interfaces. In this article, we summarize the technical challenges and opportunities for developing EVEs, and argue that they are essential for addressing the consequences of climate change

Recent progress in understanding and predicting Atlantic decadal climate variability

Recent Atlantic climate prediction studies are an exciting new contribution to an extensive body of research on Atlantic decadal variability and predictability that has long emphasized the unique role of the Atlantic Ocean in modulating the surface climate. We present a survey of the foundations and frontiers in our understanding of Atlantic variability mechanisms, the role of the Atlantic Meridional Overturning Circulation (AMOC), and our present capacity for putting that understanding into practice in actual climate prediction systems

Earth Virtualization Engines (EVE)

To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change

Low-precision climate computing: preserving information despite fewer bits

Progress towards more reliable weather and climate forecasts is limited by the resolution of numerical models and the complexity of simulated processes. Performance is therefore a major bottleneck and current models are not com- putationally efficient. High precision calculations are unnecessary, despite be- ing the standard, given the uncertainties in the climate system and the errors from discretisation, data assimilation and unresolved climate processes. In this thesis, we advance several aspects of low-precision climate computing to preserve information despite fewer bits: An information-preserving com- pression is developed that distinguishes between real and false information to reduce the very large volume of climate data produced by numerical mod- els, while minimising information loss. The bitwise real information content estimates the minimum required precision in climate data, which depends on the variable and is lower than the standard precision-levels of floating-point numbers. The impact of rounding errors introduced by different low-precision arithmetics with deterministic or stochastic rounding modes is analysed in chaotic dynamical systems. Standard floating-point numbers are not the best number format for weather and climate simulations. However, alternatives, such as posits, exist, but it is unclear whether the large effort needed to de- velop the respective hardware for future supercomputers is justified given the moderate advantage they provide in our applications. A much more central issue towards 16-bit climate models is the design of low precision-resilient al- gorithms. A naive transition to 16 bits either fails or was found to cause issues like amplified gravity waves, a change in geostrophy or rounding errors that grow as quickly as discretisation errors. However, many of these issues are found to be preventable with techniques such as scaling or a compensated time integration. Combining techniques, we develop a 16-bit fluid circulation model that approaches 4x speedups on Fujitsu’s A64FX processor compared to 64 bits, despite minimal rounding errors. The result of this thesis show that there is little reason to assume that 16-bit weather and climate models are not possible. While the design of models to compute and output only the bitwise real information is challenging, it will be a major step towards computationally efficient digital twins of the Earth’s climate system

Posits as an alternative to floats for weather and climate models

Posit numbers, a recently proposed alternative to floating-point numbers, claim to have smaller arithmetic rounding errors in many applications. By studying weather and climate models of low and medium complexity (the Lorenz system and a shallow water model) we present benefits of posits compared to floats at 16 bit. As a standardised posit processor does not exist yet, we emulate posit arithmetic on a conventional CPU. Using a shallow water model, forecasts based on 16-bit posits with 1 or 2 exponent bits are clearly more accurate than half precision floats. We therefore propose 16 bit with 2 exponent bits as a standard posit format, as its wide dynamic range of 32 orders of magnitude provides a great potential for many weather and climate models. Although the focus is on geophysical fluid simulations, the results are also meaningful and promising for reduced precision posit arithmetic in the wider field of computational fluid dynamics

Development of Nickel Alloys Based on Alloy 617 for Components in 700°C Power Plants

AbstractHigh Temperature Nickel based alloy 617 (A 617) is considered as a candidate material for the 700°C power plants due to its combination of creep strength and good fabricability. The alloy has been investigated in numerous publicly sponsored and privately financed programmes with regard to its use in USC boilers. Based on the gained experience, the material has been tailored to fit the specific purpose of USC boilers. The first development step resulted in a modified version of Alloy 617 labeled Alloy 617 B, with creep strength at 700°C about 25% higher than that listed by ASME and VdTÜV. A second step focused on the definition of welding procedures and heat treatment, demonstrating that crack formation in large welded components can be avoided by suitable post-weld heat treatment. This paper will briefly describe the effect of boron and heat-treatment on the mechanical behaviour of Alloy 617 within the scope of the German USC boiler programmes