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91探花
Relative vorticity in SpeedyWeather, painted like clouds.

Milan Kloewer (he|him)

NERC Research Fellow

Research theme

  • Climate physics

Sub department

  • Atmospheric, Oceanic and Planetary Physics

Research groups

  • Climate processes
milan.kloewer@physics.ox.ac.uk
  • About
  • Publications

Apparent temperature and heat鈥恟elated illnesses during international athletic championships: A prospective cohort study

Scandinavian Journal of Medicine and Science in Sports Wiley 31:11 (2021) 2092-2102

Authors:

Karsten Hollander, Milan Kl枚wer, Andy Richardson, Laurent Navarro, S茅bastien Racinais, Volker Scheer, Andrew Murray, Pedro Branco, Toomas Timpka, Astrid Junge, Pascal Edouard

Compressing atmospheric data into its real information content

Nature Computational Science Springer Nature 1:11 (2021) 713-724

Authors:

Milan Kl枚wer, Miha Razinger, Juan J Dominguez, Peter D D眉ben, Tim N Palmer

Quantifying aviation鈥檚 contribution to global warming

Environmental Research Letters IOP Publishing 16:10 (2021) 104027-104027

Authors:

M Kl枚wer, MR Allen, DS Lee, SR Proud, L Gallagher, A Skowron

Abstract:

Abstract 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 CO 2 and non-CO 2 effects like nitrogen oxide emissions and contrail formation to analyse aviation鈥檚 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 CO 2 . Aviation is projected to cause a total of about 0.1 掳C of warming by 2050, half of it to date and the other half over the next three decades, should aviation鈥檚 pre-COVID growth resume. The industry would then contribute a 6%鈥17% share to the remaining 0.3 掳C鈥0.8 掳C to not exceed 1.5 掳C鈥2 掳C of global warming. Under this scenario, the reduction due to COVID-19 to date is small and is projected to only delay aviation鈥檚 warming contribution by about five years. But the leveraging impact of growth also represents an opportunity: aviation鈥檚 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.

Climate Modelling in Low-Precision: Effects of both Deterministic & Stochastic Rounding

ArXiv 2104.15076 (2021)

Authors:

E Adam Paxton, Matthew Chantry, Milan Kl枚wer, Leo Saffin, Tim Palmer

Number formats, error mitigation, and scope for 16鈥恇it arithmetics in weather and climate modeling analyzed with a shallow water model

Journal of Advances in Modeling Earth Systems American Geophysical Union 12:10 (2020) e2020MS002246

Authors:

Pd D眉ben, Tn Palmer

Abstract:

The need for high鈥恜recision calculations with 64鈥恇it or 32鈥恇it floating鈥恜oint arithmetic for weather and climate models is questioned. Lower鈥恜recision numbers can accelerate simulations and are increasingly 91探花ed by modern computing hardware. This paper investigates the potential of 16鈥恇it arithmetic when applied within a shallow water model that serves as a medium complexity weather or climate application. There are several 16鈥恇it number formats that can potentially be used (IEEE half precision, BFloat16, posits, integer, and fixed鈥恜oint). It is evident that a simple change to 16鈥恇it arithmetic will not be possible for complex weather and climate applications as it will degrade model results by intolerable rounding errors that cause a stalling of model dynamics or model instabilities. However, if the posit number format is used as an alternative to the standard floating鈥恜oint numbers, the model degradation can be significantly reduced. Furthermore, mitigation methods, such as rescaling, reordering, and mixed precision, are available to make model simulations resilient against a precision reduction. If mitigation methods are applied, 16鈥恇it floating鈥恜oint arithmetic can be used successfully within the shallow water model. The results show the potential of 16鈥恇it formats for at least parts of complex weather and climate models where rounding errors would be entirely masked by initial condition, model, or discretization error.

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