  Rayleigh-B\'enard convection (RBC) is a fundamental problem of fluid
dynamics, with many applications to geophysical, astrophysical, and industrial
flows. Understanding RBC at parameter regimes of interest requires complex
physical or numerical experiments. Numerical simulations require large amounts
of computational resources; in order to more efficiently use the large numbers
of processors now available in large high performance computing clusters, novel
parallelisation strategies are required. To this end, we investigate the
performance of the parallel-in-time algorithm Parareal when used in numerical
simulations of RBC. We present the first parallel-in-time speedups for RBC
simulations at finite Prandtl number. We also investigate the problem of
convergence of Parareal with respect to to statistical numerical quantities,
such as the Nusselt number, and discuss the importance of reliable online
stopping criteria in these cases.
