Getting a weather or climate model from zero to production grade requires approximately 100 person-years, or $20M (personal experience). Because of extremely high scientific expertise needed to correctly implement such models, it's more difficult to leverage open source contributions from a broader community, like it is with web or ML frameworks. So most of the development in these projects is done by full-time hires, and to a lesser extent by contributions from early adopters.
The key technical arguments that I hear/read for such transition projects are ease of portability to accelerators (e.g. GPUs), and higher availability of programmers in the workforce.
My intuition is that a $20M budget, if carefully allocated to compiler and hardware accelerator teams, could solve running Fortran on any accelerator in its entirety.
With Fortran's tooling and compiler support for offloading standard Fortran to accelerators slowly but surely improving over time, the rationale for porting to other languages becomes increasingly more questionable and is being revisited.
But regardless of technical arguments, it's a good thing for large models and frameworks to be written and explored in diverse programming languages.