Replace ERA20C (retired) by ERA5 dataset

tests/data/input_tutorial1.yaml

@@ -14,5 +14,5 @@ optional:
   - reuse_blocks     : False
   - savefft          : False
   - dtype            : 'double'
-  - savedir          : 'spod_results'
+  - savedir          : 'results'
   - fullspectrum     : False

tutorials/README.md

@@ -24,19 +24,19 @@ aim to capture the Madden-Julian Oscillation (MJO).
 
 ### Climate
 
-#### [Tutorial: 2D Multivariate ENSO Index](climate/ERA20C_MEI_2D/ERA20C_MEI_2D.ipynb)
+#### [Tutorial: 2D Multivariate ENSO Index](climate/ERA5_MEI_2D/ERA5_MEI_2D.ipynb)
 
-This tutorial shows how to download data from an ECMWF reanalysis dataset (ERA20C),
+This tutorial shows how to download data from an ECMWF reanalysis dataset (ERA5),
 and use **PySPOD** to identify spatio-temporal coherent structured in multivariate
 2D data. In particular, we seek to identify the multivariate ENSO index (MEI).
 The data is composed by the following monthly-averaged variables: mean sea level
 pressure (MSL), zonal component of the surface wind (U10), meridional component
 of the surface wind (V10), sea surface temperature (SST), 2-meter temperature
 (T2M), and total cloud cover (TCC), on a 2D longitude-latitude grid.
 
-#### [Tutorial: 3D Quasi-Biennial Oscillation](climate/ERA20C_QBO_3D/ERA20C_QBO_3D.ipynb)
+#### [Tutorial: 3D Quasi-Biennial Oscillation](climate/ERA5_QBO_3D/ERA5_QBO_3D.ipynb)
 
-This tutorial shows how to download data from an ECMWF reanalysis dataset (ERA20C),
+This tutorial shows how to download data from an ECMWF reanalysis dataset (ERA5),
 and use **PySPOD** to identify spatio-temporal coherent structured in univariate
 3D data. In particular, we seek to identify the Quasi-Biennial Oscillation (QBO).
 The data is composed by the monthly-averages of the zonal-mean zonal winds