diff --git a/esmvaltool/diag_scripts/southern_ocean/diagnostic_acc_sigma_tr.py b/esmvaltool/diag_scripts/southern_ocean/diagnostic_acc_sigma_tr.py
new file mode 100644
index 0000000000..149a70b96e
--- /dev/null
+++ b/esmvaltool/diag_scripts/southern_ocean/diagnostic_acc_sigma_tr.py
@@ -0,0 +1,180 @@
+"""
+Look at this module for guidance how to write your own.
+
+Read the README_PERSONAL_DIAGNOSTIC file associated with this example;
+
+Module for personal diagnostics (example).
+Internal imports from exmvaltool work e.g.:
+
+from esmvalcore.preprocessor import regrid
+from esmvaltool.diag_scripts.shared.supermeans import get_supermean
+
+Pipe output through logger;
+
+Please consult the documentation for help with esmvaltool's functionalities
+and best coding practices.
+"""
+# place your module imports here:
+# import gsw
+import xarray as xr
+import numpy as np
+
+# import cmocean
+
+import logging
+
+# operating system manipulations (e.g. path constructions)
+import os
+import sys
+
+# to manipulate iris cubes
+import iris
+import matplotlib.pyplot as plt
+from esmvalcore.preprocessor import area_statistics
+
+# import internal esmvaltool modules here
+from esmvaltool.diag_scripts.shared import group_metadata, run_diagnostic
+
+# This part sends debug statements to stdout
+logger = logging.getLogger(os.path.basename(__file__))
+logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
+
+# my functions
+def _plot_transect():
+
+    pass
+
+def _compute_sigma():
+    pass
+
+def _extract_transect():
+
+    pass
+
+def main(cfg):
+
+    # # assemble the data dictionary keyed by dataset name
+    # input_data = cfg["input_data"].values()
+    # my_files_dict = group_metadata(input_data, "dataset")
+
+    # logger.info('my_files_dict: %s', my_files_dict)
+
+    logger.info('we have lift off!')
+
+    # # load xarray dataset
+    # ds = xr.open_dataset(value[0]["filename"])
+
+
+    pass
+
+
+
+
+# # example functions
+# def _plot_time_series(cfg, cube, dataset):
+#     """
+#     Example of personal diagnostic plotting function.
+
+#     Arguments:
+#         cfg - nested dictionary of metadata
+#         cube - the cube to plot
+#         dataset - name of the dataset to plot
+
+#     Returns:
+#         string; makes some time-series plots
+
+#     Note: this function is private; remove the '_'
+#     so you can make it public.
+#     """
+#     # custom local paths for e.g. plots are supported -
+#     # here is an example
+#     # root_dir = '/group_workspaces/jasmin2/cmip6_prep/'  # edit as per need
+#     # out_path = 'esmvaltool_users/valeriu/'   # edit as per need
+#     # local_path = os.path.join(root_dir, out_path)
+#     # but one can use the already defined esmvaltool output paths
+#     local_path = cfg["plot_dir"]
+
+#     # do the plotting dance
+#     plt.plot(cube.data, label=dataset)
+#     plt.xlabel("Time (months)")
+#     plt.ylabel("Area average")
+#     plt.title("Time series at (ground level - first level)")
+#     plt.tight_layout()
+#     plt.grid()
+#     plt.legend()
+#     png_name = "Time_series_" + dataset + ".png"
+#     plt.savefig(os.path.join(local_path, png_name))
+#     plt.close()
+
+#     # no need to brag :)
+#     return "I made some plots!"
+
+
+# def run_my_diagnostic(cfg):
+#     """
+#     Simple example of a diagnostic.
+
+#     This is a basic (and rather esotherical) diagnostic that firstly
+#     loads the needed model data as iris cubes, performs a difference between
+#     values at ground level and first vertical level, then squares the
+#     result.
+
+#     Before plotting, we grab the squared result (not all operations on cubes)
+#     and apply an area average on it. This is a useful example of how to use
+#     standard esmvalcore.preprocessor functionality within a diagnostic, and
+#     especially after a certain (custom) diagnostic has been run and the user
+#     needs to perform an operation that is already part of the preprocessor
+#     standard library of functions.
+
+#     The user will implement their own (custom) diagnostics, but this
+#     example shows that once the preprocessor has finished a whole lot of
+#     user-specific metrics can be computed as part of the diagnostic,
+#     and then plotted in various manners.
+
+#     Arguments:
+#         cfg - nested dictionary of metadata
+
+#     Returns:
+#         string; runs the user diagnostic
+
+#     """
+#     # assemble the data dictionary keyed by dataset name
+#     # this makes use of the handy group_metadata function that
+#     # orders the data by 'dataset'; the resulting dictionary is
+#     # keyed on datasets e.g. dict = {'MPI-ESM-LR': [var1, var2...]}
+#     # where var1, var2 are dicts holding all needed information per variable
+#     my_files_dict = group_metadata(cfg["input_data"].values(), "dataset")
+
+#     # iterate over key(dataset) and values(list of vars)
+#     for key, value in my_files_dict.items():
+#         # load the cube from data files only
+#         # using a single variable here so just grab the first (and only)
+#         # list element
+#         cube = iris.load_cube(value[0]["filename"])
+
+#         # the first data analysis bit: simple cube difference:
+#         # perform a difference between ground and first levels
+#         diff_cube = cube[:, 0, :, :] - cube[:, 1, :, :]
+#         # square the difference'd cube just for fun
+#         squared_cube = diff_cube**2.0
+
+#         # the second data analysis bit (slightly more advanced):
+#         # compute an area average over the squared cube
+#         # to apply the area average use a preprocessor function
+#         # rather than writing your own function
+#         area_avg_cube = area_statistics(squared_cube, "mean")
+
+#         # finalize your analysis by plotting a time series of the
+#         # diffed, squared and area averaged cube; call the plot function:
+#         _plot_time_series(cfg, area_avg_cube, key)
+
+#     # that's it, we're done!
+#     return "I am done with my first ESMValTool diagnostic!"
+
+
+if __name__ == "__main__":
+    # always use run_diagnostic() to get the config (the preprocessor
+    # nested dictionary holding all the needed information)
+    with run_diagnostic() as config:
+        # list here the functions that need to run
+        main(config)
diff --git a/esmvaltool/recipes/recipe_so_drakepassage.yml b/esmvaltool/recipes/recipe_so_drakepassage.yml
new file mode 100644
index 0000000000..886f81ac5e
--- /dev/null
+++ b/esmvaltool/recipes/recipe_so_drakepassage.yml
@@ -0,0 +1,52 @@
+# ESMValTool
+# recipe_so_acc_sigma_tr.yml
+---
+documentation:
+  description:
+    Creates a transect through Drake Passage of zonal velocity with
+    the sigma contour.
+  title:
+    Drake Passage zonal velocity transect with sigma contour
+
+  authors:
+    - demora_lee
+
+datasets:
+  - dataset: HadGEM3-GC31-LL
+
+preprocessors:
+  # ------------------------------------------------------------------------
+  # Preprocessor settings for Drake Passage transect
+  # ------------------------------------------------------------------------
+  time_mean:
+    climate_statistics:
+      operator: 'mean'
+      period: 'full'
+
+diagnostics:
+  # ------------------------------------------------------------------------
+  # Drake Passage zonal velocity transect with sigma contour
+  # --------------------------------------------------------------------------------
+  transect:
+    description:  Transect of zonal velocity and sigma
+    variables:
+      thetao: # Temperature ocean
+        ensemble: r1i1p1f3
+        exp: historical
+        grid: gn
+        mip: Omon
+        project: CMIP6
+        timerange: 2000/2001
+        preprocessor: time_mean
+      # so:    # Salinity ocean
+      #   ensemble: r1i1p1f3
+      #   exp: historical
+      #   grid: gn
+      #   mip: Omon
+      #   project: CMIP6
+      #   timerange: 2000/2001
+      #   preprocessor: time_mean
+
+    scripts:
+      script1:
+        script: /scratch/project_465001823/twilder/ESMValTool/esmvaltool/diag_scripts/southern_ocean/diagnostic_acc_sigma_tr.py