.. _XML_oceans:

Ocean diagnostics
=================

Overview
........

These recipes are used for evaluating the marine component of models of the
earth system. Using these recipes, it should be possible to evaluate both the
physical models and biogeochemistry models. All these recipes use the
ocean diagnostics package.

The ocean diagnostics package contains several diagnostics which produce
figures and statistical information of models of the ocean. The datasets have
been pre-processed by ESMValTool, based on recipes in the recipes directory.
Most of the diagnostics produce two or less types of figure, and several
diagnostics are called by multiple recipes.

Each diagnostic script expects a metadata file, automatically generated by
ESMValTool, and one or more pre-processed dataset. These are passed to the
diagnostic by ESMValTool in the settings.yml and metadata.yml files.

The ocean diagnostics toolkit can not figure out how to plot data by itself.
The current version requires the recipe to produce the correct pre-processed
data for each diagnostic script. ie: to produce a time series plot,
the preprocessor must produce a time-dimensional dataset.

While these tools were built to evaluate the ocean component models, they also
can be used to produce figures for other domains. However, there are some ocean
specific elements, such as the z-direction being positive and reversed, and
some of the map plots have the continents coloured in by default.

As elsewhere, both the model and observational datasets need to be
compliant with the CMOR data.

Available recipes
.................

* recipe_ocean_amoc.yml_
* recipe_ocean_example.yml_
* recipe_ocean_scalar_fields.yml_
* recipe_ocean_bgc.yml_
* recipe_ocean_quadmap.yml_
* recipe_ocean_ice_extent.yml_
* recipe_ocean_multimap.yml_


recipe_ocean_amoc.yml
---------------------

The recipe_ocean_amoc.yml_ is an recipe that produces figures describing the
Atlantic Meridional Overturning Circulation (AMOC) and the drake passage
current.

The recipes produces time series of the AMOC at 26 north and the
drake passage current.

.. centered:: |pic_amoc|

.. |pic_amoc| image:: /recipes/figures/ocean/amoc_fig_1.png


This figure shows the multi model comparison of the AMOC from several CMIP5
historical simulations, with a 6 year moving average (3 years either side of the
central value). A similar figure is produced for each individual model, and
for the Drake Passage current.

This recipe also produces a contour transect and a coloured transect plot
showing the Atlantic stream function for each individual model, and a
multi-model contour is also produced:

.. centered:: |pic_ocean_sf3| |pic_ocean_sf4|

.. |pic_ocean_sf3| image:: /recipes/figures/ocean/stream_function1.png
.. |pic_ocean_sf4| image:: /recipes/figures/ocean/stream_function2.png


recipe_ocean_example.yml
------------------------

The recipe_ocean_example.yml_ is an example recipe which shows several examples
of how to manipulate marine model data using the ocean diagnostics tools.

While several of the diagnostics here have specific uses in evaluating models,
it is meant to be a catch-all recipe demonstrating many different ways to
evaluate models.

All example calculations are performed using the ocean temperature in a three
dimensional field (thetao), or at the surface (tos). This recipe demonstrates
the use of a range of preprocessors in a marine context, and also shows many
of the standard model-only diagnostics (no observational component is included.)

This recipe includes examples of how to manipulate both 2D and 3D fields to
produce:

* Time series:

  * Global surface area weighted mean time series
  * Volume weighted average time series within a specific depth range
  * Area weighted average time series at a specific depth
  * Area weighted average time series at a specific depth in a specific region.
  * Global volume weighted average time series
  * Regional volume weighted average time series

* Maps:

  * Global surface map (from 2D ad 3D initial fields)
  * Global surface map using re-gridding to a regular grid
  * Global map using re-gridding to a regular grid at a specific depth level
  * Regional map using re-gridding to a regular grid at a specific depth level

* Transects:

  * Produce various transect figure showing a re-gridded transect plot, and multi model comparisons

* Profile:

  * Produce a Global area-weighted depth profile figure
  * Produce a regional area-weighted depth profile figure

All the these fields can be expanded using a

recipe_ocean_bgc.yml
--------------------

The recipe_ocean_bgc.yml_ is an example recipe which shows a several simple examples of how to
manipulate marine biogeochemical model data.

This recipe includes the following fields:

* Global total volume-weighted average time series:

  * temperature, salinity, nitrate, oxygen, silicate (vs WOA data) `*`
  * chlorophyll, iron, total alkalinity (no observations)

* Surface area-weighted average time series:

  * temperature, salinity, nitrate, oxygen, silicate (vs WOA data) `*`
  * fgco2 (global total), integrated primary production, chlorophyll,
    iron, total alkalinity (no observations)

* Scalar fields time series:

  * mfo (including stuff like drake passage)

* Profiles:

  * temperature, salinity, nitrate, oxygen, silicate (vs WOA data) `*`
  * chlorophyll, iron, total alkalinity (no observations)

* Maps + contours:

  * temperature, salinity, nitrate, oxygen, silicate (vs WOA data) `*`
  * chlorophyll, iron, total alkalinity (no observations)

* Transects + contours:

  * temperature, salinity, nitrate, oxygen, silicate (vs WOA data) `*`
  * chlorophyll, iron, no observations)

`*` Note that Phosphate is also available as a WOA diagnostic, but I haven't
included it as HadGEM2-ES doesn't include a phosphate field.

This recipe uses the World Ocean Atlas data, which can be downloaded from:
https://www.ncei.noaa.gov/products/world-ocean-atlas
(last access 02/08/2021)

Instructions: Select the "All fields data links (1° grid)" netCDF file,
which contain all fields.


.. recipe_OxygenMinimumZones.yml
.. ------------------------------------------
.. This recipe will appear in a future version.

.. This recipe produces an analysis of Marine oxygen. The diagnostics are based on
.. figure 1 of the following work:
.. Cabré, A., Marinov, I., Bernardello, R., and Bianchi, D.: Oxygen minimum zones
.. in the tropical Pacific across CMIP5 models: mean state differences and climate
.. change trends, Biogeosciences, 12, 5429-5454,
.. https://doi.org/10.5194/bg-12-5429-2015, 2015.


recipe_ocean_quadmap.yml
------------------------

The recipe_ocean_quadmap.yml_ is an example recipe showing the
diagnostic_maps_quad.py_ diagnostic.
This diagnostic produces an image showing four maps. Each of these four maps
show latitude vs longitude and the cube value is used as the colour scale.
The four plots are:

=================   ====================
model1              model 1 minus model2
-----------------   --------------------
model2 minus obs    model1 minus obs
=================   ====================

These figures are also known as Model vs Model vs Obs plots.


The figure produced by this recipe compares two versions of the HadGEM2 model
against ATSR sea surface temperature:

.. centered:: |pic_quad_plot|

.. |pic_quad_plot| image:: /recipes/figures/ocean/ocean_quad_plot1.png

This kind of figure can be very useful when developing a model, as it
allows model developers to quickly see the impact of recent changes
to the model.


recipe_ocean_ice_extent.yml
---------------------------

The recipe_ocean_ice_extent.yml_ recipe produces several metrics describing
the behaviour of sea ice in a model, or in multiple models.

This recipe has four preprocessors, a combinatorial combination of

* Regions: Northern or Southern Hemisphere
* Seasons: December-January-February or June-July-August

Once these seasonal hemispherical fractional ice cover is processed,
the resulting cube is passed 'as is' to the diagnostic_seaice.py_
diagnostic.

This diagnostic produces the plots:

* Polar Stereographic projection Extent plots of individual models years.
* Polar Stereographic projection maps of the ice cover and ice extent for
  individual models.
* A time series of Polar Stereographic projection Extent plots - see below.
* Time series plots of the total ice area and the total ice extent.


The following image shows an example of the sea ice extent plot, showing the
Summer Northern hemisphere ice extent for the HadGEM2-CC model, in the
historical scenario.

.. centered:: |pic_sea_ice1|

.. |pic_sea_ice1| image:: /recipes/figures/ocean/ocean_sea_ice1.png


The sea ice diagnostic is unlike the other diagnostics in the ocean diagnostics
toolkit. The other tools are build to be generic plotting tools which
work with any field (ie ``diagnostic_timeseries.py`` works fine for Temperature,
Chlorophyll, or any other field. On the other hand, the
sea ice diagnostic is the only tool that performs a field specific evaluation.

The diagnostic_seaice.py_ diagnostic is more fully described below.

recipe_ocean_multimap.yml
-------------------------

The recipe_ocean_multimap.yml_ is an example recipe showing the
diagnostic_maps_multimodel.py_ diagnostic.
This diagnostic produces an image showing Model vs Observations maps or
only Model fields when observational data are not provided.
Each map shows latitude vs longitude fields and user defined values are used to set the colour scale.
Plot layout can be modified by modifying the `layout_rowcol` argument.

The figure produced by this recipe compares the ocean surface CO2 fluxes
for 16 different CMIP5 model against Landschuetzer2016 observations.

The diagnostic_maps_multimodel.py_ diagnostic is documented below.


Available diagnostics
........................

Diagnostics are stored in the diag_scripts directory: ocean_.

The following python modules are included in the ocean diagnostics package.
Each module is described in more detail both below and inside the module.

- diagnostic_maps.py
- diagnostic_maps_quad.py
- diagnostic_model_vs_obs.py
- diagnostic_profiles.py
- diagnostic_seaice.py
- diagnostic_timeseries.py
- diagnostic_tools.py
- diagnostic_transects.py
- diagnostic_maps_multimodel.py


diagnostic_maps.py
------------------

The diagnostic_maps.py_ produces a spatial map from a NetCDF. It requires the
input netCDF to have the following dimensions. Either:

- A two dimensional file: latitude, longitude.
- A three dimensional file: depth, latitude, longitude.

In the case of a 3D netCDF file, this diagnostic produces a map for EVERY layer.
For this reason, we recommend extracting a small number of specific layers in
the preprocessor, using the `extract_layer` preprocessor.

This script can not process NetCDFs with multiple time steps. Please use the
`climate_statistics` preprocessor to collapse the time dimension.

This diagnostic also includes the optional arguments, `threshold` and
`thresholds`.

- threshold: a single float.
- thresholds: a list of floats.

Only one of these arguments should be provided at a time. These two arguments
produce a second kind of diagnostic map plot: a contour map showing the spatial
distribution of the threshold value, for each dataset. Alternatively, if the
thresholds argument is used instead of threshold, the single-dataset contour
map shows the contours of all the values in the thresholds list.

If multiple datasets are provided, in addition to the single dataset contour,
a multi-dataset contour map is also produced for each value in the thresholds
list.

Some appropriate preprocessors for this diagnostic would be:

For a  Global 2D field:

  .. code-block:: yaml

      prep_map_1:
	climate_statistics:


For a  regional 2D field:

  .. code-block:: yaml

	prep_map_2:
	    extract_region:
	      start_longitude: -80.
	      end_longitude: 30.
	      start_latitude: -80.
	      end_latitude: 80.
        climate_statistics:
          operator: mean

For a  Global 3D field at the surface and 10m depth:

  .. code-block:: yaml

	prep_map_3:
	  custom_order: true
	  extract_levels:
	    levels: [0., 10.]
	    scheme: linear_horizontal_extrapolate_vertical
      climate_statistics:
        operator: mean


For a multi-model comparison mean of 2D global fields including contour thresholds.

  .. code-block:: yaml

	prep_map_4:
	  custom_order: true
      climate_statistics:
        operator: mean
	  regrid:
	    target_grid: 1x1
	    scheme: linear

And this also requires the threshold key in the diagnostic:

  .. code-block:: yaml

	diagnostic_map:
	  variables:
	    tos: # Temperature ocean surface
	      preprocessor: prep_map_4
	      field: TO2M
	  scripts:
	    Ocean_regrid_map:
	      script: ocean/diagnostic_maps.py
	      thresholds: [5, 10, 15, 20]


diagnostic_maps_quad.py
--------------------------------

The diagnostic_maps_quad.py_ diagnostic produces an image showing four maps.
Each of these four maps show latitude vs longitude and the cube value is used
as the colour scale. The four plots are:

=================   ====================
model1              model 1 minus model2
-----------------   --------------------
model2 minus obs    model1 minus obs
=================   ====================


These figures are also known as Model vs Model vs Obs plots.

This diagnostic assumes that the preprocessors do the bulk of the
hard work, and that the cubes received by this diagnostic (via the settings.yml
and metadata.yml files) have no time component, a small number of depth layers,
and a latitude and longitude coordinates.

An appropriate preprocessor for a 2D field would be:

  .. code-block:: yaml

	prep_quad_map:
        climate_statistics:
            operator: mean

and an example of an appropriate diagnostic section of the recipe would be:

  .. code-block:: yaml

	diag_map_1:
	  variables:
	    tos: # Temperature ocean surface
	      preprocessor: prep_quad_map
	      field: TO2Ms
	      mip: Omon
	  additional_datasets:
	#        filename: tos_ATSR_L3_ARC-v1.1.1_199701-201112.nc
	#        download from: https://datashare.is.ed.ac.uk/handle/10283/536
	    - {dataset: ATSR,  project: obs4MIPs,  level: L3,  version: ARC-v1.1.1,  start_year: 2001,  end_year: 2003, tier: 3}
	  scripts:
	    Global_Ocean_map:
	      script: ocean/diagnostic_maps_quad.py
	      control_model: {dataset: HadGEM2-CC, project: CMIP5, mip: Omon, exp: historical, ensemble: r1i1p1}
	      exper_model: {dataset: HadGEM2-ES, project: CMIP5, mip: Omon, exp: historical, ensemble: r1i1p1}
	      observational_dataset: {dataset: ATSR, project: obs4MIPs,}

Note that the details about the control model, the experiment models
and the observational dataset are all provided in the script section of the
recipe.



diagnostic_model_vs_obs.py
--------------------------------

The diagnostic_model_vs_obs.py_ diagnostic makes model vs observations maps
and scatter plots. The map plots shows four latitude vs longitude maps:

========================     =======================
Model                        Observations
------------------------     -----------------------
Model minus Observations     Model over Observations
========================     =======================

Note that this diagnostic assumes that the preprocessors do the bulk of the
hard work, and that the cube received by this diagnostic (via the settings.yml
and metadata.yml files) has no time component, a small number of depth layers,
and a latitude and longitude coordinates.

This diagnostic also includes the optional arguments, `maps_range` and
`diff_range` to manually define plot ranges. Both arguments are a list of two floats
to set plot range minimun and maximum values respectively for Model and Observations
maps (Top panels) and for the Model minus Observations panel (bottom left).
Note that if input data have negative values the Model over Observations map
(bottom right) is not produced.

The scatter plots plot the matched model coordinate on the x axis, and the
observational dataset on the y coordinate, then performs a linear
regression of those data and plots the line of best fit on the plot.
The parameters of the fit are also shown on the figure.

An appropriate preprocessor for a 3D+time field would be:

  .. code-block:: yaml

	preprocessors:
	  prep_map:
	    extract_levels:
	      levels:  [100., ]
	      scheme: linear_extrap
        climate_statistics:
          operator: mean
	    regrid:
	      target_grid: 1x1
	      scheme: linear



diagnostic_maps_multimodel.py
-----------------------------

The diagnostic_maps_multimodel.py_ diagnostic makes model(s) vs observations maps
and if data are not provided it draws only model field.

It is always nessary to define the overall layout trough the argument `layout_rowcol`,
which is a list of two integers indicating respectively the number of rows and columns
to organize the plot. Observations has not be accounted in here as they are automatically
added at the top of the figure.

This diagnostic also includes the optional arguments, `maps_range` and
`diff_range` to manually define plot ranges. Both arguments are a list of two floats
to set plot range minimun and maximum values respectively for variable data and
the Model minus Observations range.

Note that this diagnostic assumes that the preprocessors do the bulk of the
hard work, and that the cube received by this diagnostic (via the settings.yml
and metadata.yml files) has no time component, a small number of depth layers,
and a latitude and longitude coordinates.

An appropriate preprocessor for a 3D+time field would be:

  .. code-block:: yaml

        preprocessors:
          prep_map:
            extract_levels:
              levels:  [100., ]
              scheme: linear_extrap
        climate_statistics:
          operator: mean
            regrid:
              target_grid: 1x1
              scheme: linear



diagnostic_profiles.py
--------------------------------

The diagnostic_profiles.py_ diagnostic produces images of the profile over time from a cube.
These plots show cube value (ie temperature) on the x-axis, and depth/height
on the y axis. The colour scale is the annual mean of the cube data.
Note that this diagnostic assumes that the preprocessors do the bulk of the
hard work, and that the cube received by this diagnostic (via the settings.yml
and metadata.yml files) has a time component, and depth component, but no
latitude or longitude coordinates.

An appropriate preprocessor for a 3D+time field would be:

  .. code-block:: yaml

	preprocessors:
	  prep_profile:
	    extract_volume:
	      long1: 0.
	      long2:  20.
	      lat1:  -30.
	      lat2:  30.
	      z_min: 0.
	      z_max: 3000.
	    area_statistics:
              operator: mean



diagnostic_timeseries.py
--------------------------------

The diagnostic_timeseries.py_ diagnostic produces images of the time development
of a metric from a cube. These plots show time on the x-axis and cube value
(ie temperature) on the y-axis.

Two types of plots are produced: individual model timeseries plots and
multi model time series plots. The individual plots show the results from a
single cube, even if this cube is a multi-model mean made by the `multimodel`
preprocessor.

The multi model time series plots show several models on the same axes, where
each model is represented by a different line colour. The line colours are
determined by the number of models, their alphabetical order and the `jet`
colour scale. Observational datasets and multimodel means are shown as black
lines.

This diagnostic assumes that the preprocessors do the bulk of the work,
and that the cube received by this diagnostic (via the settings.yml
and metadata.yml files) is time-dimensional cube. This means that the pre-processed
netcdf has a time component, no depth component, and no latitude or longitude
coordinates.

Some appropriate preprocessors would be :

For a global area-weighted average 2D field:

  .. code-block:: yaml

	area_statistics:
          operator: mean

For a global volume-weighted average 3D field:

  .. code-block:: yaml

	volume_statistics:
          operator: mean

For a global area-weighted surface of a 3D field:

  .. code-block:: yaml

	extract_levels:
	  levels: [0., ]
	  scheme: linear_horizontal_extrapolate_vertical
	area_statistics:
          operator: mean


An example of the multi-model time series plots can seen here:

.. centered:: |pic_amoc2|

.. |pic_amoc2| image:: /recipes/figures/ocean/amoc_fig_1.png



diagnostic_transects.py
--------------------------------



The diagnostic_transects.py_ diagnostic produces images of a transect,
typically along a constant latitude or longitude.

These plots show 2D plots with either latitude or longitude along the x-axis,
depth along the y-axis and and the cube value is used as the colour scale.


This diagnostic assumes that the preprocessors do the bulk of the hard work,
and that the cube received by this diagnostic (via the settings.yml and
metadata.yml files) has no time component, and one of the latitude or
longitude coordinates has been reduced to a single value.

An appropriate preprocessor for a 3D+time field would be:

  .. code-block:: yaml

    climate_statistics:
      operator: mean
    extract_slice:
      latitude: [-50.,50.]
      longitude: 332.

Here is an example of the transect figure:
.. centered:: |pic_ocean_sf1|

.. |pic_ocean_sf1| image:: /recipes/figures/ocean/stream_function1.png

And here is an example of the multi-model transect contour figure:

.. centered:: |pic_ocean_sf2|

.. |pic_ocean_sf2| image:: /recipes/figures/ocean/stream_function2.png



diagnostic_seaice.py
--------------------------------



The diagnostic_seaice.py_ diagnostic is unique in this module, as it produces
several different kinds of images, including time series, maps, and contours.
It is a good example of a diagnostic where the preprocessor does very little
work, and the diagnostic does a lot of the hard work.

This was done purposely, firstly to demonstrate the flexibility of ESMValTool,
and secondly because Sea Ice is a unique field where several Metrics can be
calculated from the sea ice cover fraction.

The recipe Associated with with diagnostic is the recipe_SeaIceExtent.yml.
This recipe contains 4 preprocessors which all perform approximately the same
calculation. All four preprocessors extract a season:
- December, January and February (DJF)
- June, July and August (JJA)
and they also extract either the North or South hemisphere. The four
preprocessors are combinations of DJF or JJA and North or South hemisphere.

One of the four preprocessors is North Hemisphere Winter ice extent:

.. code-block:: yaml

	timeseries_NHW_ice_extent: # North Hemisphere Winter ice_extent
	  custom_order: true
	  extract_time: &time_anchor # declare time here.
	      start_year: 1960
	      start_month: 12
	      start_day: 1
	      end_year: 2005
	      end_month: 9
	      end_day: 31
	  extract_season:
	    season: DJF
	  extract_region:
	    start_longitude: -180.
	    end_longitude: 180.
	    start_latitude: 0.
	    end_latitude: 90.

Note that the default settings for ESMValTool assume that the year starts on the
first of January. This causes a problem for this preprocessor, as the first
DJF season would not include the first Month, December, and the final would not
include both January and February. For this reason, we also add the
`extract_time` preprocessor.

This preprocessor group produces a 2D field with a time component, allowing
the diagnostic to investigate the time development of the sea ice extend.

The diagnostic section of the recipe should look like this:

.. code-block:: yaml

	diag_ice_NHW:
	  description: North Hemisphere Winter Sea Ice diagnostics
	  variables:
	    sic: # surface ice cover
	      preprocessor: timeseries_NHW_ice_extent
	      field: TO2M
	      mip: OImon
	  scripts:
	    Global_seaice_timeseries:
	      script: ocean/diagnostic_seaice.py
	      threshold: 15.

Note the the threshold here is 15%, which is the standard cut of for the
ice extent.

The sea ice diagnostic script produces three kinds of plots, using the
methods:

- `make_map_extent_plots`: extent maps plots of individual models using a Polar Stereographic project.
- `make_map_plots`: maps plots of individual models using a Polar Stereographic project.
- `make_ts_plots`: time series plots of individual models

There are no multi model comparisons included here (yet).



diagnostic_tools.py
-------------------



The diagnostic_tools.py_ is a module that contains several python tools used
by the ocean diagnostics tools.

These tools are:

- folder: produces a directory at the path provided and returns a string.
- get_input_files: loads a dictionary from the input files in the metadata.yml.
- bgc_units: converts to sensible units where appropriate (ie Celsius, mmol/m3)
- timecoord_to_float: Converts time series to decimal time ie: Midnight on January 1st 1970 is 1970.0
- add_legend_outside_right: a plotting tool, which adds a legend outside the axes.
- get_image_format: loads the image format, as defined in the global user config.yml.
- get_image_path: creates a path for an image output.
- make_cube_layer_dict: makes a dictionary for several layers of a cube.

We just show a simple description here, each individual function is more fully
documented in the diagnostic_tools.py_ module.


A note on the auxiliary data directory
......................................

Some of these diagnostic scripts may not function on machines with no access
to the internet, as cartopy may try to download the shape files. The solution
to this issue is the put the relevant cartopy shapefiles in a directory which
is visible to esmvaltool, then link that path to ESMValTool via
the `auxiliary_data_dir` variable in your config-user.yml file.

The cartopy masking files can be downloaded from:
https://www.naturalearthdata.com/downloads/


In these recipes, cartopy uses the 1:10, physical coastlines and land files::

      110m_coastline.dbf
      110m_coastline.shp
      110m_coastline.shx
      110m_land.dbf
      110m_land.shp
      110m_land.shx


Associated Observational datasets
........................................

The following observations datasets are used by these recipes:

World Ocean ATLAS
-----------------
These data can be downloaded from:
https://www.nodc.noaa.gov/OC5/woa13/woa13data.html
(last access 10/25/2018)
Select the "All fields data links (1° grid)" netCDF file, which contain all
fields.

The following WOA datasets are used by the ocean diagnostics:
 - Temperature
 - Salinity
 - Nitrate
 - Phosphate
 - Silicate
 - Dissolved Oxygen

These files need to be reformatted using the `esmvaltool data format WOA` command.


Landschuetzer 2016
------------------
These data can be downloaded from:
https://www.nodc.noaa.gov/archive/arc0105/0160558/3.3/data/0-data/spco2_1982-2015_MPI_SOM-FFN_v2016.nc
(last access 09/20/2022)

The following variables are used by the ocean diagnostics:
 - fgco2, Surface Downward Flux of Total CO2
 - spco2, Surface Aqueous Partial Pressure of CO2
 - dpco2, Delta CO2 Partial Pressure

The file needs to be reformatted using the `esmvaltool data format Landschuetzer2016` command.



.. Links:

.. Recipes:
.. _recipe_ocean_amoc.yml: https://github.com/ESMValGroup/ESMValTool/tree/main/esmvaltool/recipes/recipe_ocean_amoc.yml
.. _recipe_ocean_example.yml: https://github.com/ESMValGroup/ESMValTool/tree/main/esmvaltool/recipes/recipe_ocean_example.yml
.. _recipe_ocean_scalar_fields.yml: https://github.com/ESMValGroup/ESMValTool/tree/main/esmvaltool/recipes/recipe_ocean_scalar_fields.yml
.. _recipe_ocean_bgc.yml: https://github.com/ESMValGroup/ESMValTool/tree/main/esmvaltool/recipes/recipe_ocean_bgc.yml
.. _recipe_ocean_quadmap.yml: https://github.com/ESMValGroup/ESMValTool/tree/main/esmvaltool/recipes/recipe_ocean_quadmap.yml
.. _recipe_ocean_Landschuetzer2016.yml: https://github.com/ESMValGroup/ESMValTool/tree/main/esmvaltool/recipes/recipe_ocean_Landschuetzer2016.yml
.. _recipe_ocean_multimap.yml: https://github.com/ESMValGroup/ESMValTool/tree/main/esmvaltool/recipes/recipe_ocean_multimap.yml

.. Diagnostics:
.. _ocean: https://github.com/ESMValGroup/ESMValTool/tree/main/esmvaltool/diag_scripts/ocean/:
.. _diagnostic_maps.py: https://github.com/ESMValGroup/ESMValTool/tree/main/esmvaltool/diag_scripts/ocean/diagnostic_maps.py
.. _diagnostic_maps_quad.py: https://github.com/ESMValGroup/ESMValTool/tree/main/esmvaltool/diag_scripts/ocean/diagnostic_maps_quad.py
.. _diagnostic_model_vs_obs.py: https://github.com/ESMValGroup/ESMValTool/tree/main/esmvaltool/diag_scripts/ocean/diagnostic_model_vs_obs.py
.. _diagnostic_maps_multimodel.py: https://github.com/ESMValGroup/ESMValTool/tree/main/esmvaltool/diag_scripts/ocean/diagnostic_maps_multimodel.py
.. _diagnostic_profiles.py: https://github.com/ESMValGroup/ESMValTool/tree/main/esmvaltool/diag_scripts/ocean/diagnostic_profiles.py
.. _diagnostic_timeseries.py: https://github.com/ESMValGroup/ESMValTool/tree/main/esmvaltool/diag_scripts/ocean/diagnostic_timeseries.py
.. _diagnostic_transects.py: https://github.com/ESMValGroup/ESMValTool/tree/main/esmvaltool/diag_scripts/ocean/diagnostic_transects.py
.. _diagnostic_seaice.py: https://github.com/ESMValGroup/ESMValTool/tree/main/esmvaltool/diag_scripts/ocean/diagnostic_seaice.py
.. _diagnostic_tools.py: https://github.com/ESMValGroup/ESMValTool/tree/main/esmvaltool/diag_scripts/ocean/diagnostic_tools.py