AWS Webinar - cmip6_preprocessing

Transcript

1. So what is in the CMIP archive? Model A Model B Model C Member i Member j Member k Slightly di ff erent initial conditions, physical parameter … 30+ for some models 88 models - Di ff erent code, grids, physical/biological/chemical parameterizations
2. Typical CMIP6 science workflow Custom Analysis applied to each model and member
4. But CMIP6 is not quite clean enough
5. 😖
6. 😖 😎
7. Why cmip6_preprocessing? Based on the xarray datamodel Labelled arrays and datasets - ‘pandas for n-dimensional arrays’ Integrates with the existing pangeo stack where possible (regionmask, xgcm, xesmf, pint-coming soon) Lightweight, dask friendly Works great in the cloud, but also locally, on HPC...wherever you have xarray really.
8. Components `preprocessing` for dataset cleaning/homogenization `postprocessing` to combine the model data into xarray datasets with e.g. several members or combined historical+forced experiments `regionmask` create basin masks for arbitrary model output `drift_removal` to compute trends for preindustrial control runs and remove that drift from other output.
9. Demo
10. I ❤ Feedback, questions, contributions. Docs Github @JuliusBusecke jbusecke julius@ldeo.columbia.edu