Model skills notebook with ERA5

It was a nice idea, but I will not champion it.

Research question (thanks Iuliia Polkova!) "Evaluation of prediction skill for surface air temperature and precipitation from the CMIP6 models using ERA5"

STEP 1. DONE find ERA in dkrz data pool --> /pool/data/ERA5/ (thanks Fabi! also for the docs: https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation#ERA5:datadocumentation-Introduction)

STEP 2. DONE how to load ERA5 --> no problem with GRIB: http://xarray.pydata.org/en/stable/examples/ERA5-GRIB-example.html (thanks Marco!)

STEP 3. DONE load model data --Y intake and xarray, just copy-paste from the summer days nb's

STEP 4. TODO how to compare ERA-model: Iuliia's answer: "load the data, calculate ensemble mean, calculate anomalies with respect to the long-term mean, calculate RMSE or CORR for different lead times et voila. If you have problems just let me know, I will help. The skill is usually calculated for anomalies and for lead times, so for prediction year 1, and multi-year averages such as years 2-5. Depending on the research problem, we calculate skill for each grid-point or for the anomalies averaged over a certain region. Here is one of my papers with the examples of skill calculation https://doi.org/10.1029/2018MS001439"