Precipitation rate, convective diagnostics and spin-up compared across physics suites in the model uncertainty model intercomparison project (MUMIP)

A parameterisation suite is the combination of all parameterisation schemes that is used by a numerical model of the atmosphere. These parameterisation (or “physics”) suites are widely seen as the most uncertain components of atmospheric models.  

In MUMIP we compare deterministic parameterisation suites from across different modelling centres under common prescribed large-scale dynamics. In the first MUMIP experiment, these dynamical tendencies have been derived by coarse-graining the convection-permitting ICON DYAMOND simulation to 0.2 degree resolution. We use these realistic spatiotemporal dynamical patterns to drive millions  of single column model simulations over the tropical Indian Ocean with prescribed SSTs. We use this data to estimate the uncertainty from their physics across four models, each using their default convection-parametrised physics suites. The models are: IFS, GFS, RAP and ARPEGE.

The distributions of precipitation rate, convective available potential energy (CAPE), convective inhibition (CIN) and level of neutral buoyancy are analysed, as well as individual model tendencies and rate of change of CAPE and CIN as a function of lead time and, for instance, the diurnal cycle . We find notable differences across the physics suites and even more strongly between convection-parameterised physics suites and the convection-permitting ICON DYAMOND benchmark. Furthermore, we relate these diagnostics to biases in temperature and specific humidity. We also develop a framework for the detection of statistical relations among diagnostics and/or their change. The framework may for instance be used to quantify the impact of spin-up compared to persistence ("memory") and randomness within a dataset and to identify similarity in the physics across modelling centres.

In this contribution some of the early results of the international MUMIP project will be presented and we hope to encourage other researchers to use and/or complement the data of MUMIP. Please refer to https://mumip.web.ox.ac.uk for details of how to get involved.