Skip high-volume data transfer and access free computing resources for your CMIP6 multi-model analyses

Tired of downloading tons of model results? Is your internet connection flakey? Are you about to overload your computer’s memory with the constant increase of data volume and you need more computing resources? You can request free of charge computing time at one of the supercomputers of the Infrastructure of the European Network of Earth System modelling (IS-ENES)¹, the European part of Earth System Grid Federation (ESGF)², which also hosts and maintains more than 6 Petabytes of CMIP6 and CORDEX data.

Thanks to this new EU Commission funded service, you can run your own scripts in your favorite programming language and straightforward pre- and post-process model data. There is no need for heavy data transfer, just load with one line of code the data slice you need because your script will directly access the data pool. Therefore, days-lasting calculations will be done in seconds. You can test the service, we very easily provide pre-access activities.

In this session we will run Jupyter notebooks directly on the German Climate Computing Center (DKRZ)³, one of the ENES high performance computers and a ESGF data center, showing how to load, filter, concatenate, take means, and plot several CMIP6 models to compare their results, use some CMIP6 models to calculate some climate indexes for any location and period, and evaluate model skills with observational data. We will use Climate Data Operators (cdo)⁴ and Python packages for Big Data manipulation, as Intake⁵, to easily extract the data from the huge catalog, and Xarray⁶, to easily read NetDCF files and scale to parallel computing. We are continuously creating more use cases for multi-model evaluation, mechanisms of variability, and impact analysis, visit the demos, find more information, and apply here: https://portal.enes.org/data/data-metadata-service/analysis-platforms.

[1] https://is.enes.org/
[2] https://esgf.llnl.gov/
[3] https://www.dkrz.de/
[4] https://code.mpimet.mpg.de/projects/cdo/
[5] https://intake.readthedocs.io/en/latest/
[6] http://xarray.pydata.org/en/stable/