nemo-cookbook 2026.3.0b1

Recipes for reproducible analysis of NEMO ocean general circulation model outputs using xarray.

Reproducible analysis of NEMO ocean general circulation model outputs using xarray.

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About

NEMO Cookbook extends the familiar xarray data model with grid-aware data structures designed for performing reproducible analyses of the Nucleus for European Modelling of the Ocean (NEMO) ocean general circulation model outputs.

Our aim is to provide a collection of recipes implementing the post-processing & analysis functions available in CDFTOOLS alongside new diagnostics (e.g., surface-forced water mass transformation), which are compatible with generalised vertical coordinate systems (e.g., MES).

Each recipe uses the NEMODataTree and NEMODataArray structures to leverage xarray, flox & dask libraries (think of these are your cooking utensils) to calculate a diagnostic with NEMO ocean model outputs (i.e., the raw ingredients - that's where you come in!).

NEMO Data Structures

At the core of NEMO Cookbook are two abstractions:

• NEMODataTree → a hierarchical container for organising NEMO model outputs extending the xarray.DataTree.
• NEMODataArray → a NEMO grid-aware extension of xarray.DataArray.

If you already use xarray, NEMO Cookbook should feel immediately natural:

• NEMODataTree builds directly on xarray.DataTree.
• NEMODataArray behaves like xarray.DataArray.
• All standard xarray operations are still available!

What’s new is that these objects understand the NEMO grid, meaning you no longer need to manually track:

• which NEMO model grid a variable belongs to (e.g., T, U, V, F, W).
• how variables relate across NEMO model grids.
• where to find grid scale factors.
• how to consistently apply grid-aware operations.

NEMODataTree

NEMODataTree is an extension of the xarray.DataTree object and an alternative to the xgcm grid object.

NEMODataTree organises NEMO model outputs into a single, coherent data structure, where each node in the tree represents an xarray.Dataset of variables from one NEMO model grid. This allows us to:

• Store output variables defined on NEMO T, U, V, W, F grids using the model’s native (i, j, k) curvilinear coordinate system.
• Analyse parent, child and grandchild domains of nested configurations using a single DataTree.
• Pre-process model outputs (i.e., removing ghost points and generating t/u/v/f masks without needing a mesh_mask file).

NEMODataArray

NEMODataArray extends xarray.DataArray to give each variable knowledge of its:

• NEMO model grid location (e.g., T, U, V, W, F)
• parent NEMODataTree
• associated NEMO grid metrics (grid scale factors)

This knowledge enables reproducible grid-aware computation. For example, a NEMODataArray can be used to:

• Automatically access correct grid metrics.
• Apply operators (e.g., derivative, integral) as formulated in NEMO.
• Calculate grid-aware diagnostics, including masked & binned statistics.
• Perform vertical grid coordinate transformations via conservative interpolation.

Crucially, this happens without changing how you write xarray code — you still work with labeled arrays, but with far more NEMO understanding behind the scenes.

Getting Started

Installation

Users are recommended to installing NEMO Cookbook into a new virtual environment via GitHub:

pip install git+https://github.com/NOC-MSM/nemo_cookbook.git

Alternatively, users can clone the latest version of the nemo_cookbook repository using Git:

git clone [e-mail]:NOC-MSM/nemo_cookbook.git

Then, install the dependencies in a new conda virtual environment and pip install NEMO Cookbook in editable mode:

cd nemo_cookbook

conda env create -f environment.yml
conda activate env_nemo_cookbook

pip install -e .

Usage

NEMO Cookbook is designed to make complex grid-aware analysis of NEMO model outputs feel as simple as working with standard xarray objects.

Pre-Processing Made Simple

• Create a NEMODataTree from the National Oceanography Centre's eORCA1 JRA55v1 ocean sea-ice hindcast simulation stored in Analysis-Ready Cloud Optimised (ARCO) Zarr stores...

# Open eORCA1 NEMO domain_cfg:
ds_domain = xr.open_zarr("https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/domain_cfg", consolidated=True, chunks={})

# Open eORCA1 NEMO gridT dataset:
ds_gridT = xr.open_zarr("https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/T1y")

# Define dictionary of grid datasets defining eORCA1 parent model domain:
datasets = {"parent": {"domain": ds_domain, "gridT": ds_gridT}}

# Initialise new NEMODataTree with zonally periodic parent domain north-folding on F-points:
nemo = NEMODataTree.from_datasets(datasets=datasets, iperio=True, nftype="F", read_mask=True)

Exploring NEMO Model Outputs

• Access land-sea masked conservative temperature variable defined on NEMO model T-grid points as a NEMODataArray...

nemo["gridT/thetao_con"].masked

• Access NEMO grid scale factors of zonal velocity variable defined on NEMO model U-grid points...

nemo["gridU/uo"].metrics

• Access familiar xarray operations...

nemo["gridT/tos_con"].mean(dim="time_counter")

Calculating Grid-Aware Diagnostics

• Calculate meridional ocean heat transport using a constant reference density rho0 and specific heat capacity of seawater cp0...

(rho0 * cp0 * nemo["gridT/thetao_con"].transform_to(to='V') * nemo["gridV/vo"]).integral(dim=["i", "k"])

• Transform conservative temperature variable thetao_con defined on a NEMO model T-point from it's native 75 z*-levels to regularly spaced geopotential levels at 200 m intervals...

# Define target vertical grid cell thicknesses:
e3t_target = xr.DataArray(np.repeat(200.0, 30), dims=['k_new'])

# Transform conservative temperature to new vertical coordinate system:
nemo["gridT/thetao_con"].transform_vertical_grid(e3_new = e3t_target)

Documentation

To learn more about NEMO Cookbook & to start exploring our current recipes, visit our documentation here.

Recipes

NEMO Cookbook recipes are Jupyter Notebooks available to view statically in our documentation or download and edit via the recipes/ directory:

Available Recipes:

• Meridional overturning stream function in an arbitrary tracer coordinates.

• Meridional overturning stream function in depth coordinates (z/z*).

• Upper ocean heat content.

• Meridional heat & salt transports.

• Surface-forced water mass transformation in potential density coordinates.

• Volume census in T-S coordinates.

• Masked statistics using bounding boxes and polygons.

• Extracting volume transports and properties along the Overturning in the Subpolar North Atlantic array.

• Vertical coordinate transformations.

• Barotropic stream functions.

Recipes In Development:

• Meridional overturning stream functions in depth coordinates (MEs).

• Mixed layer heat content.

• Sea ice diagnostics.

• Vorticity diagnostics.

Funding

The ongoing development of NEMO Cookbook is funded by the following projects:

• AtlantiS: Atlantic Climate and Environment Strategic Science
• ARIA - PROMOTE: Progressing earth system Modelling for Tipping Point Early warning systems
• EPOC: Explaining & Predicting the Ocean Conveyor

Contact

Ollie Tooth ([e-mail])