How do these models address overfitting? I'm sure this is taken good care of, but from just reading the article it almost sounds like some details were added to make it fit recent changes better.
You don't really optimise parameters for large scale climate models, you put in your best guess at the initial values and then wait for weeks to months depending on your goal. With function evaluations that costly, I don't really see how you have time to overfit.
The big worry is bias, a lot of assumptions about how physics can be approximated are needed to make these models, but there isn't time to test every combination of them at the full scale. So you do small scale tests and hope nothing unexpected happens in the full run.
For scale, a performance basline paper from 2018 reports achiving 0.23 simulated years per wall clock day using 4900 GPU cores [1].
SiempreViernes|6 years ago
The big worry is bias, a lot of assumptions about how physics can be approximated are needed to make these models, but there isn't time to test every combination of them at the full scale. So you do small scale tests and hope nothing unexpected happens in the full run.
For scale, a performance basline paper from 2018 reports achiving 0.23 simulated years per wall clock day using 4900 GPU cores [1].
[1]: https://www.geosci-model-dev.net/11/1665/2018/gmd-11-1665-20...