The first MUMIP exercise is underway. For this exercise, the high resolution simulation produced for the DYAMOND summer simulations using ICON at 2.5 km resolution will serve as benchmark. This simulation has been coarse-grained to 0.2o resolution over a region in the tropical Indian Ocean, 51-95oE, 35oS-5oN, selected to largely avoid land regions. 

For more information about the experimental design please see this white paper and introductory video on YouTube by Hannah Christensen.



Accessing input data


All coarse-grained SCM input files are hosted at DKRZ on the Swift system. Data are publically available and can be downloaded from the mumip container


Swift does not support directory structures, so filenames have been made as descriptive as possible:

mumip_[high-res model descriptor]_[region]_[coarse resolution]_[date].[hour]_[code version].nc



Each file contains all variables within the chosen domain, at a single time. For convenience, a single test file has been uploaded with multiple timesteps at a single lat-lon point, indicated 'onecol'


Accessing SCM run data


Details will be added here once SCM data are available for general analysis.



The software used in MUMIP are freely available to interested users, consisting of the following:


Coarse graining software

NCL software to produce pseudo-DEPHY format SCM forcing fields from a high-resolution simulation.


SCM automation

Running thousands of SCM simulations efficiently over many gridded input datasets is a technical challenge. While partners are free to produce their own code to optimise this, we provide some sample python scripts written by Andrew Dawson (ECMWF) for this paper to support these efforts.

“scmtiles”: Python software to deploy many independent SCMs over a domain.

“openifs-scmtiles”: Python software to deploy the OpenIFS SCM using scmtiles.

Note: openifs-scmtiles has not yet been updated to receive DEPHY-format input.




Christensen, H. M., Dawson, A., & Holloway, C. E. (2018). Forcing Single-Column Models Using High-Resolution Model Simulations. Journal of Advances in Modeling Earth Systems, 10(8), 1833–1857.

Christensen, H. M. (2020). Constraining stochastic parametrisation schemes using high-resolution simulations. Quarterly Journal of the Royal Meteorological Society146(727).