Technical milestone reached: global earth system simulations with 1.2 km resoln (opens in new tab)

Simulation, Consciousness, Existence

Hans Moravec, 1998.

https://frc.ri.cmu.edu/~hpm/project.archive/general.articles...

[1]: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2017MS00...

Simulation of dynamic systems is a big deep area. In general you use what is called numerical simulation where you have a model describing your system, in the form of a partial differential equal equations.

You start with the chosen initial conditions, choose a delta-t as your time increment, and solve the equation for those inputs. That result is the input to the next iteration.

The most basic algorithm to solve such an equation is “Newton’s method” but no one actually uses that, they use many more advanced methods. But if you are learning that is where you start.

This approach has advanced greatly over the last 70 years. Doing numerical simulation is why early computing work got funding, to simulated nuclear reaction inside bombs.

Now numerical simulation is the occupation of all the worlds top super computers. It’s used for climate simulation, bridge strength, how sky scrapers flex in the wind, testing car crashes or even simulating the strength of ceramics. Oh and it used a lot in financial simulations to model risk and calculate the price of assets.

You initialize the system at some known state (I.e. set the temperature, pressure, etc. at all grid points to real world measurements) and then integrate a complex differential equation for the next time step and so forth. So it is not like a automaton. Finite elements analysis comes closer, but I think they use a different scheme like finite volume methods.

A lot of insight can be gained by [this](https://pure.mpg.de/rest/items/item_3379183/component/file_3...) paper. The first 10 pages should give you a rough overview.

Roughly speaking yes. Divide all into a grid of cells. Model a cell state with a bunch of numbers, apply some rules to update cell state with neighbors. The trick is to figure out rules of updating state. One needs to write differential equations first, incorporating all relevant physical processes into them, and to transform equations into those rules of updating, which will be a way more complex than with Game of Life.

Though it may be even more complex, like different time steps at different time-points, or changing a grid of cells to increase details in some areas where much is going on by slowing down simulation. Most of complications are due to a limited abilities of our computers: the idea to get more precision by calculating less.

The Earth system model provides a numerical laboratory for research on the climate dynamics on time scales of a season to millennia. Necessarily most processes are parameterized to allow the computationally efficient integration over long periods.

It's also mentioned it will contribute to DestinE[2]:

Destination Earth (DestinE) aims to develop – on a global scale - a highly accurate digital model of the Earth to monitor and predict the interaction between natural phenomena and human activities. [...] The initial focus will be on the effects of climate change and extreme weather events, their socio-economic impact and possible adaptation and mitigation strategies.

[1]: https://mpimet.mpg.de/en/science/models/icon-esm/

[2]: https://digital-strategy.ec.europa.eu/en/policies/destinatio...

The modeling system in the linked article is a high-fidelity numerical simulation of the coupled Earth system. It's a giant PDE solver for Navier-Stokes applied to the Earth's atmospheres and oceans, coupled together with a great deal of additional physics simulation. The intent is to reproduce, in simulation, the Earth's atmospheric and oceans with the highest fidelity. This set of simulations is the culmination of nearly 70 years of investment, going back to the very first applications of digital computers for solving complex math equations (one of the first simulations bought for ENIAC was a crude quasi-geostrophic atmospheric mode / weather forecast).

NVIDIA's FourCastNet, while very cool, is quite literally a facsimile of this type of system. It's really not even in the same ballpark.

The difficulty of making a "large" map comes from what you want to simulate and in how much detail, not from how big it is per se.

There's a LOT more I could ask about her, and that's just one character. This show is fan fiction LOOSELY based on the writings. VERY loosely.

That is all. I'll enjoy my ban now.

> Our ICON-ESM configuration is already used in production mode for scientific purpose with horizontal resolutions of 10 km, 5 km and 2.5 km. With the 1.2 km configuration we have now opened the door for a new class of numerical models which will allow us to investigate local impacts of climate change, such as extremes of precipitation, storms and droughts.

Some evidence of them using 10 km cells and then subdividing into halves, gets you down to 1.25 km.