We present a collection of climate/CMIP themed notebooks that show off how to access and analyse climate data collections available at the NCI.
Jupyter Notebook availability
NCI filesystem path: /g/data/dk92/notebooks/climate-cmip
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filename | description | dataset | data project to join |
|---|---|---|---|
animation_01_time_series.ipynb | Creates a time series animation using the ACCESS-CM2 model output prepared for CMIP6 | fs38 | |
animation_02_monthly_temperature_anomally.ipynb | Create Australian monthly temperature anomaly animation over 1850-2005 using Celluloid | ||
animation_03_correlation_cmip6_era5.ipynb | Show the correlation of precipitation between CMIP6 model simulation and observation data from ERA5 | ||
cdo_01_calculate_monthly_anomaly_and_nino34_index_cmip6.ipynb | Calculate monthly anomaly and Nino34 index using CDO | ||
cdo_02_compare_model_and_observation_cmip6.ipynb | Use CDO to compare model and observation data | ||
cdo_03_ocean_land_mask_cmip6.ipynb | Create land and ocean masks to limit the field to land/ocean values | ||
hdfview_cmip6.ipynb | View the content of a netCDF file and create a hdf file from scratch | ||
| iris_ssp_AUS_maps.ipynb | Uses the IRIS Python package to plot maps of ensemble anomalies for SSP585 future senario | ||
| iris_ssp_tas.ipynb | Uses IRIS Python package to calculate and plot global average temperature trend for SSP585 future senario | ||
| nco_cmip5.ipynb | Quick view and manipulations of data using NCO | rr3 | |
| panoply_cmip5.ipynb | Use NASA's data viewer Panoply to view file contents and metadata information | rr3 | |
| paraview_cmip5.ipynb | Visualise data in Paraview | rr3 | |
| plot_pandas_climate_obs.ipynb | Climate observation data visualisation using Pandas | The Australian Climate Observations Reference Network (ACORN) | yj45 |
xarray notebooks
The xarray notebooks listed in the table below are designed to be run on both NCI's OOD and using Pangeo on Gadi:
| filename | description | dataset | data project to join |
|---|---|---|---|
xarray_01_data_access_CMIP5.ipynb | Use xarray to read a single file or multiple files on both the NCI g/data file system and through NCI's THREDDS Data server | rr3 | |
| xarray_02_subset_slicing_plot_CMIP6.ipynb | Use xarray to enable label based subsetting and slicing of data | oi10 | |
xarray_03_Australian_temperature_precipitation_change_CMIP6.ipynb | Compare the temperature and precipitation change in Australia under two different future scenarios | oi10 | |
| xarray_04_calculate_metrics_CMIP6.ipynb | Calculate climate metrics such as the monthly climatology, monthly anomalies and mean anomalies over a certain period | fs38 | |
xarray_05_calculate_Nino34_time_series_for_ARCCSS1-3_CMIP5.ipynb | Calculate Nino34 time series using the CMIP5 ARCCSS1.3 models | rr3 | |
xarray_06_calculate_Nino34_time_series_for_CESM2_CMIP6.ipynb | Calculate Nino34 time series using the CMIP6 CESM2 models | oi10 | |
xarray_07_statistical_resample_roll_climatology_CMIP6.ipynb | Run common statistical operations such as resampling, rolling mean and standard deviation; Using subplots and visualising data with Cartopy | oi10 | |
xarray_08_model_uncertainty_CMIP6.ipynb | Evaluate CMIP6 model uncertainty | oi10 |
Dask notebooks
The Dask notebooks listed in the table below are designed to be run on both NCI's VDI and using a parallel-enabled data analysis environment on Gadi. Some of these examples are compute and memory intensive and will greatly benefit in performance by scaling up using a dask cluster.
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