Speaker
Description
In the Model Uncertainty-MIP (MUMIP) we run single column models from different modelling centres over the same period and domain with a series of 6hr simulations. By constructing the SCM initial and boundary conditions such that they are derived from a common 3D-simulation, a common prescription of dynamics is enforced. Consequently, combined dataset of the array of SCM-simulations will mimic a series of fully 3D NWP runs. Such a model intercomparison with objective procedures is highly important to understand and quantify uncertainty in physical parameterisations and parameterisation packages under the same large-scale state. It can further help in constraining stochasticity when coarse simulations are compared to storm-resolving simulations (Christensen, 2020). A dataset covering the Indian Ocean is currently under construction for the SCM of ECMWF, the UK Met Office, Météo France, and the NCAR/NOAA Developmental Testbed Centre.
Here, we present a first test case for MUMIP data. Recent work has demonstrated differences in (non-)stationarity across different reanalysis datasets (notably ERA5 and the Japanese reanalysis) in particular regarding the climatology of tropical explicit/convective precipitation and CAPE (Buschow 2024). A proposed hypothesis is that data assimilation and spin-up from non-native model states could be responsible for the non-stationary reanalysis climate.
We analyse MUMIP data to investigate similar transient behavior as a function of forecast time and the diurnal cycle. We further investigate the potential of a link between transience in short-term forecasts of (convective) precipitation and CAPE during the spin-up of the SCMs. Furthermore, MUMIP datasets allow us to intercompare different model physics packages as a function of lead time (0-6 hours), to quantify their divergence and to broadly illustrate model physics uncertainty.
