Updated to add (sadly): Just 2 days before this article was published, Dr. Ivo Babuška, pioneer of the numerical solution of CFD equations, and of a-posteriori error analysis for mesh refinment, passed away (April 12, 2023), at the age of 97. He was a pre-former colleague who retired from UMCP in 1994 or 1996 (a couple years before I got onboard). He uncovered the formerly unknown eponymous Babuška–Brezzi (“inf-sup”) condition required for solving Navier-Stokes (and other saddle-point systems) whereby velocities need to be discretized at a higher-order (eg. 2nd-order) than pressure (eg. 1st-order) to prevent wild checkerboard oscillations in the solution. He would probably get a kick to know that, becasue of him, more than 400 billion simultaneous equations will now need to be solved by the SCREAM model!

Great point! Chaos theory means sensitivity to initial conditions (via non-negative Lyapunov exponents), and therefore, the more accurately one knows current conditions everywhere in a domain (vx, vy, vz, T, P, H2O vapor, at time=0), the farther into the future they can produce accurate predictions for the subsequent temporal evolution of the system (eg. predict weather accurately for 10-days into the future). However, even without “infinitely” accurate ICs (initial conditions), solution of the system for a substantial time span has the potential to resolve the phase-space attractor of the system (eg. Lorentz attractor) which determines all of its possible future states, and hence every possible weather that can occur in the target domain — which is still mighty useful (and awesome)!

It’s not pure unadulterated chaos thanks to the fluid viscosity of air (or its eddy viscosity computed from appropriate turbulence closures) which makes the flow dissipative on the whole: tornadoes, hurricanes, monsoons, and the like, eventually die-out as a result — but their frequency of occurence, and their intensity, may increase or decrease depending on global conditions of warming, cooling, and mis-exfoliation (eh-eh-eh!).

Correction: make that approx. 400 billion simultaneous equations … as this is (I think) 3-D Navier-Stokes plus heat and moisture transport, and therefore (at least) 6 dependent vars per node (vx, vy, vz, P, T, H2O vapor), mapped over 65 billion nodes … Definitely awe-inspiring stuff!

I’ll add that the grid-Courant-number limit on accurate computations constrains time-step size to possibly less than 1/100 of an hour (for up to 100 km/h winds over a 1km grid), that is roughly 30 seconds, and means approximately 1 million time steps per year (plus nonlinearity iterations). Shorter time steps (and more of them per year) would be needed for higher maximum wind speeds (in conventional FDM/FEM).

We have seen the radical effect AMD’s chiplets have had on the economics & performance of CPUs since Zen arrived in 2017. I am confident of similar gains as GPU chiplets evolve now the ice is broken.

I too get this feeling, a lot of times; there seems to be a lot of duplicity in promoters of this and that new zeitgeist, and what not, being fashionable means X, and you’re excluded if different, non-conforming. In such situations it helps me to relate back to folks whose opinion I value, for whichever reason, here Sandra Snan (of Chicken Scheme) — she (can be crude) writes this: “Everything is on the cinder unless we all sober the fuck up wrt climate change and slam the breaks on emissions!” — admitedly her opinion, but I value it!

The domain discretization of the spherical shell is somewhat anisotropic. In the horizontal, it is 1 node per 10 km^2 (or approx 3.2 km between nodes, in x and y directions), with an upcoming target of 1 node per km^2 (1 km between nodes, in x and y). So they’re looking at 510 million nodes in x-y to cover earth’s 510 million km^2 surface area. The node spacing is finer in the z-direction (vertical), and ranges from 50 m between nodes near the surface, to 250 m between nodes in the stratosphere (at 40km or 60km top elevation), for a total of 72 vertical nodes for E3SM, and 128 such nodes for SCREAM. Accordingly, with SCREAM, there are 65 billion simultaneous equations to be solved (and iterated over for nonlinearity) per time step! WoW!

I don’t think the E3SM people are doing that, but clearly there is a way to interleave AI in the ensembles to do an effective speedup.

Well played….