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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

Github: https://github.com/NCI-data-analysis-platform/climate-cmip

To preview these notebooks: https://nbviewer.jupyter.org/github/NCI-data-analysis-platform/climate-cmip/tree/main/

The notebooks in the table below show how to use various tools available on NCI's OOD environment

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

ESGF CMIP6 Australian Data

fs38

animation_02_monthly_temperature_anomally.ipynb

Create Australian monthly temperature anomaly animation over 1850-2005 using Celluloid

ESGF CMIP6 Australian Data

fs38

animation_03_correlation_cmip6_era5.ipynb

Show the correlation of precipitation between CMIP6 model simulation and observation data from ERA5

ESGF CMIP6 Replication Data


ERA5 Replicated Data

oi10


rt52

cdo_01_calculate_monthly_anomaly_and_nino34_index_cmip6.ipynb

Calculate monthly anomaly and Nino34 index using CDO

ESGF CMIP6 Replication Data

oi10

cdo_02_compare_model_and_observation_cmip6.ipynb

Use CDO to compare model and observation data

ESGF CMIP6 Replication Data

oi10

cdo_03_ocean_land_mask_cmip6.ipynb

Create land and ocean masks to limit the field to land/ocean values

ESGF CMIP6 Replication Data

oi10

hdfview_cmip6.ipynb

View the content of a netCDF file and create a hdf file from scratch

ESGF CMIP6 Replication Data

oi10

iris_ssp_AUS_maps.ipynbUses the IRIS Python package to plot maps of ensemble anomalies for SSP585 future senario

ESGF CMIP6 Replication Data

oi10

iris_ssp_tas.ipynbUses IRIS Python package to calculate and plot global average temperature trend for SSP585 future senario

ESGF CMIP6 Replication Data

oi10

nco_cmip5.ipynb

Quick view and manipulations of data using NCO

ESGF CMIP5 Australian Data

rr3
panoply_cmip5.ipynbUse NASA's data viewer Panoply to view file contents and metadata information

ESGF CMIP5 Australian Data

rr3
paraview_cmip5.ipynb Visualise data in Paraview

ESGF CMIP5 Australian Data

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: 

filenamedescriptiondataset

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

ESGF CMIP5 Australian Data

rr3
xarray_02_subset_slicing_plot_CMIP6.ipynbUse xarray to enable label based subsetting and slicing of data

ESGF CMIP6 Replication Data

oi10

xarray_03_Australian_temperature_precipitation_change_CMIP6.ipynb

Compare the temperature and precipitation change in Australia under two different future scenarios

ESGF CMIP6 Replication Data

oi10
xarray_04_calculate_metrics_CMIP6.ipynb

Calculate climate metrics such as the monthly climatology, monthly anomalies and mean anomalies over a certain period 

ESGF CMIP6 Australian Data

fs38

xarray_05_calculate_Nino34_time_series_for_ARCCSS1-3_CMIP5.ipynb

Calculate Nino34 time series using the CMIP5 ARCCSS1.3 models

ESGF CMIP5 Australian Data

rr3

xarray_06_calculate_Nino34_time_series_for_CESM2_CMIP6.ipynb

Calculate Nino34 time series using the CMIP6 CESM2 models

ESGF CMIP6 Replication Data

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 

ESGF CMIP6 Replication Data

oi10

xarray_08_model_uncertainty_CMIP6.ipynb

Evaluate CMIP6 model uncertainty 

ESGF CMIP6 Replication Data

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.

filenamedescriptiondataset

data project

to join

dask_data_chunks_CMIP6.ipynb

Dask array basics; NetCDF chunks vs Dask chunks; chunking practices

ESGF CMIP6 Replication Data

oi10
dask_diagnositc_tools.ipynb

Introduce a few diagnostic tools such as visualising task graphs, local and distributed diagnostics tools

ESGF CMIP6 Australian Data

fs38
dask_intensive_calculation_cmip6.ipynb

Explore some of the Coupled Model Intercomparison Project (CMIP6) replication data to demonstrate how Dask handles expensive calculations

ESGF CMIP6 Replication Data

oi10
dask_interactive_visualisation_CMIP6.ipynb

Calculate time and zonal mean of the temperature of CMIP6 GFDL models and interactively visualise data

ESGF CMIP6 Replication Data

oi10
dask_memory_compute_management.ipynbStrategies of managing larger-than-memory data using partition; saving data onto disk; cleaning ram; executing in the background

ESGF CMIP6 Replication Data

oi10
dask_xarray_CMIP6.ipynb

Use standard xarray operations on Dask Array; persist data into memory to speed up I/O; customise workflows and automatic parallelisation

ESGF CMIP6 Australian Data

fs38
dask_xarray_precipitation.ipynbCalculate the intra-ensemble range for all the mean daily temperature and average seasonal precipitation in Australia using historical precipitation data of the CESM2 model within CMIP6

ESGF CMIP6 Australian Data

fs38
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