Next Genera*on Compu*ng: Needs and Opportuni*es for Weather, Climate, and Atmospheric Sciences. David Randall

Transcription

1 Next Genera*on Compu*ng: Needs and Opportuni*es for Weather, Climate, and Atmospheric Sciences David Randall

2 Way back I first modified, ran, and analyzed results from an atmospheric GCM in The model ran on an IBM 360/91, which had < 10 MB of memory and delivered < 10 Mflops of cpu power. The model spun latitude belts of data in and out of disk, because there was nowhere near enough memory to hold the full grid. You could tell which part of the model was executing by the sounds that the disk drives were making.

3 Two ways that changes in the computational landscape are driving changes in models More resolution More emphasis on the physics

4 Where technology is leading us Clock speed, MHz

5 Where technology is leading us Clock speed, MHz It used to be true that when you got a new computer, the additional speed could be used either to refine the grid or to make longer runs on the same grid.

6 Where technology is leading us Clock speed, MHz It used to be true that when you got a new computer, the additional speed could be used either to refine the grid or to make longer runs on the same grid. No more.

7 Where technology is leading us Clock speed, MHz It used to be true that when you got a new computer, the additional speed could be used either to refine the grid or to make longer runs on the same grid. No more. Technology trends now encourage us to drastically refine our grids, but are less compatible with dramatically longer runs on the existing grids.

8 Resolve clouds? Modest increases in resolution don t improve the simulation of cloud processes. A cloud-resolving model needs a horizontal grid-spacing of 4 km or finer.

9 The yin and yang of climate modeling Physics Dynamics Dynamics refers to fluid dynamics that is explicitly simulated on the grid. Physics refers all other processes, which have to be parameterized.

10 Physics Dynamics

11 Where does the computer time go? 50% Physics Dynamics 50%

12 Where does the computer time go? 50% Physics Dynamics 50% This is changing.

13 Two kinds of scaling Strong scaling Physics Weak scaling Dynamics Model architecture should be designed to take advantage of the strong scaling of the physics.

14 Strong scaling: You can run faster at a given resolution by adding more cores. This is great. The physics scales strongly because all columns are independent; no communication is needed. Load balancing doesn't affect this conclusion because the percentage of idle cores is independent of resolution. Weak scaling: You can run a higher-resolution model by adding more cores. This is good. The dynamics scales weakly per time step. But the number of time steps required for a given simulation length increases as the resolution increases.

15 Two kinds of scaling Strong scaling Physics Weak scaling Dynamics Model architecture should be designed to take advantage of the strong scaling of the physics.

16 So here s what we should do 99% Physics Dynamics 1% Moderate increases in the resolution of the weakly scaling dynamical core Major increases in the realism of the strongly scaling physics

17 Into the Grey Zone The nature of the sub-grid physical processes depends on the grid size. Low res: dx ~ 100 km, high res: dx ~ 1 km Parameterizations for low-res models are not appropriate for high-res models, and viceversa. Because of this, existing lowres models do not converge in the mathematical sense. Can we create parameterizations that are resolution-independent and convergent?

18 The status of scale-aware physics The community has some ideas about how to do this. None of the ideas has been satisfactorily tested. Only part of the problem has been addressed so far.

19 Two ways that changes in the computational landscape are driving changes in models More resolution More emphasis on the physics

20 Extra slides

21 High-resolution models make Big Data. In addition to the problems of storing and transporting Big Data, there is the problem of understanding what it means.

22 High-resolution models make Big Data. In addition to the problems of storing and transporting Big Data, there is the problem of understanding what it means. AI can find patterns in the data, although we may not understand how it found them.

23 High-resolution models make Big Data. In addition to the problems of storing and transporting Big Data, there is the problem of understanding what it means. AI can find patterns in the data, although we may not understand how it found them. That s like finding gold without understanding how you found it. Could be worse.

24 Regional refinement Works well in engineering applications. May work for weather prediction. May work for ocean climate. May not work well for atmosphere climate, because the physics is important everywhere.

25 Limited benefits of increasing resolution without changing the parameterizations Error Climate km 20 km 2 km Horizontal Grid Spacing NWP

26 Microphysics is the end game. Particles scatter, emit, and absorb radiation. Particle growth leads to precipitation. Particles host heterogeneous reactions. No matter how fast computers become, we will always need microphysics parameterizations.

27 Coexistence going forward Conventional models 1965 Superparameterized models 2001 Global cloudresolving models 2025?

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