edit: Actually, in the scenarios you'd use a supercomputer for, the added latency and overhead (shoddy servers, network, etc.) would most likely make the run time orders of magnitude higher.
There are many existing valuable codes written in FORTRAN. They work, it's not worth the investment to replace them with something else.
Second, many of the codes are in C++, not FORTRAN. Not clear that's any less of a problem.
By codes they mean -- at the minimum -- pretty much anything that requires frequent communication between any or all nodes as a necessary part of computation. (For example, simulations across a large 3D space, where the changing states of particles on node A directly impacts the states of particles on adjacent nodes.)
Also, there is a wide range of literature about communication patterns for supercomputer apps; my argument is that often times, to solve the problem that matters, you may not actually need to run the simulation you think you do. It's more that people are just used to running that way.
For example, with MD, you can run 1 sim parallelized over 100 machines using tightly coupled communication (doesn't necessarily mean the forces and positions of every particle have to be shared between node decompositions) or run 100 sims over 100 machines, with no communication except for input and output files. The latter can often answer the same question far more cheaply.
I don't want to drag this out, but where do you see the language constraint? You need an MPI binding, sure, but what else?
Supercomputers aren't built so that people can squander the resource (desktop PCs, closest clusters, and phones fulfill that role).
Anyway, the issue with JVMs is that they don't have predictable performance, not that the compilers can't be ported.
There are still some fortran libraries in large scale use for this sort of thing. They are still in use because they are very good, and replacing them would be very expensive for little gain.