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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
personal website
github
  • About
  • Publications

Neural General Circulation Models for Weather and Climate

(2023)

Authors:

Dmitrii Kochkov, Janni Yuval, Ian Langmore, Peter Norgaard, Jamie Smith, Griffin Mooers, Milan Klöwer, James Lottes, Stephan Rasp, Peter Düben, Sam Hatfield, Peter Battaglia, Alvaro Sanchez-Gonzalez, Matthew Willson, Michael P Brenner, Stephan Hoyer
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An analysis of ways to decarbonize conference travel after COVID-19

Nature Nature Research 583 (2020) 356-360

Authors:

Milan Klower, Deborah Hopkins, Myles Allen, James Higham
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Fluid simulations accelerated with 16 bits: Approaching 4x speedup on A64FX by squeezing ShallowWaters.jl into Float16

Journal of Advances in Modelling Earth Systems Wiley 14:2 (2022) e2021MS002684

Authors:

Milan Kloewer, Sam Hatfield, Matteo Croci, Peter D Düben, Tim N Palmer

Abstract:

Most Earth-system simulations run on conventional central processing units in 64-bit double precision floating-point numbers Float64, although the need for high-precision calculations in the presence of large uncertainties has been questioned. Fugaku, currently the world's fastest supercomputer, is based on A64FX microprocessors, which also support the 16-bit low-precision format Float16. We investigate the Float16 performance on A64FX with ShallowWaters.jl, the first fluid circulation model that runs entirely with 16-bit arithmetic. The model implements techniques that address precision and dynamic range issues in 16 bits. The precision-critical time integration is augmented to include compensated summation to minimize rounding errors. Such a compensated time integration is as precise but faster than mixed precision with 16 and 32-bit floats. As subnormals are inefficiently supported on A64FX the very limited range available in Float16 is 6 × 10−5 to 65,504. We develop the analysis-number format Sherlogs.jl to log the arithmetic results during the simulation. The equations in ShallowWaters.jl are then systematically rescaled to fit into Float16, using 97% of the available representable numbers. Consequently, we benchmark speedups of up to 3.8x on A64FX with Float16. Adding a compensated time integration, speedups reach up to 3.6x. Although ShallowWaters.jl is simplified compared to large Earth-system models, it shares essential algorithms and therefore shows that 16-bit calculations are indeed a competitive way to accelerate Earth-system simulations on available hardware.
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A neural network-based Universal Thermal Climate Index for reliable global thermal-stress classification across extreme weather

ArXiv 2609.35949 (2026)

Authors:

Bikem Pastine, Milan Klöwer, Tianning Tang, Sarah Wilson Kemsley, Louise Slater
Details from ArXiV

How Do AI Climate Models Respond to Warming Across Climate Zones?

(2026)

Authors:

Charlotte C Merchant, Milan Klöwer, Bradley Stanley-Clamp, Maren Höver, Simon LL Michel, Edward Groot, Hannah M Christensen
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