diff --git a/.github/workflows/benchmarks_run.yml b/.github/workflows/benchmarks_run.yml
index 626db393db..dfb2c7c33a 100644
--- a/.github/workflows/benchmarks_run.yml
+++ b/.github/workflows/benchmarks_run.yml
@@ -71,9 +71,9 @@ jobs:
         with:
           fetch-depth: 0
 
-      - name: Install ASV & Nox
+      - name: Install Nox
         run: |
-          pip install asv nox
+          pip install nox
 
       - name: Cache environment directories
         id: cache-env-dir
@@ -112,7 +112,7 @@ jobs:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           PR_NUMBER: ${{ github.event.number }}
         run: |
-          python benchmarks/bm_runner.py branch origin/${{ github.base_ref }}
+          nox -s benchmarks -- branch origin/${{ github.base_ref }}
 
       - name: Run overnight benchmarks
         id: overnight
@@ -128,7 +128,7 @@ jobs:
           
           if [ "$first_commit" != "" ]
           then
-            python benchmarks/bm_runner.py overnight $first_commit
+            nox -s benchmarks -- overnight $first_commit
           fi
 
       - name: Warn of failure
diff --git a/.github/workflows/ci-manifest.yml b/.github/workflows/ci-manifest.yml
index 888fc6e478..0fe5b7f950 100644
--- a/.github/workflows/ci-manifest.yml
+++ b/.github/workflows/ci-manifest.yml
@@ -23,4 +23,4 @@ concurrency:
 jobs:
   manifest:
     name: "check-manifest"
-    uses: scitools/workflows/.github/workflows/ci-manifest.yml@2024.05.0
+    uses: scitools/workflows/.github/workflows/ci-manifest.yml@2024.06.0
diff --git a/.github/workflows/refresh-lockfiles.yml b/.github/workflows/refresh-lockfiles.yml
index d2c1fcdcff..6abd4cbc6c 100644
--- a/.github/workflows/refresh-lockfiles.yml
+++ b/.github/workflows/refresh-lockfiles.yml
@@ -14,5 +14,5 @@ on:
 
 jobs:
   refresh_lockfiles:
-    uses: scitools/workflows/.github/workflows/refresh-lockfiles.yml@2024.05.0
+    uses: scitools/workflows/.github/workflows/refresh-lockfiles.yml@2024.06.0
     secrets: inherit
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index fb33180953..df449ae010 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -29,7 +29,7 @@ repos:
     -   id: no-commit-to-branch
 
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.4.4"
+    rev: "v0.4.7"
     hooks:
     -   id: ruff
         types: [file, python]
@@ -38,7 +38,7 @@ repos:
         types: [file, python]
 
 -   repo: https://github.com/codespell-project/codespell
-    rev: "v2.2.6"
+    rev: "v2.3.0"
     hooks:
     -   id: codespell
         types_or: [asciidoc, python, markdown, rst]
diff --git a/benchmarks/README.md b/benchmarks/README.md
index 54e580c8b1..ed0b6497c6 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -20,13 +20,13 @@ the PR's base branch, thus showing performance differences introduced
 by the PR. (This run is managed by 
 [the aforementioned GitHub Action](../.github/workflows/benchmark.yml)).
 
-`asv ...` commands must be run from this directory. You will need to have ASV
-installed, as well as Nox (see
-[Benchmark environments](#benchmark-environments)).
-
-The benchmark runner ([bm_runner.py](./bm_runner.py)) provides conveniences for
+To run locally: the **benchmark runner** provides conveniences for
 common benchmark setup and run tasks, including replicating the automated 
-overnight run locally. See `python bm_runner.py --help` for detail.
+overnight run locally. This is accessed via the Nox `benchmarks` session - see
+`nox -s benchmarks -- --help` for detail (_see also: 
+[bm_runner.py](./bm_runner.py)_). Alternatively you can directly run `asv ...`
+commands from this directory (you will still need Nox installed - see
+[Benchmark environments](#benchmark-environments)).
 
 A significant portion of benchmark run time is environment management. Run-time
 can be reduced by placing the benchmark environment on the same file system as
@@ -43,11 +43,17 @@ if it is not already. You can achieve this by either:
 
 * `OVERRIDE_TEST_DATA_REPOSITORY` - required - some benchmarks use
 `iris-test-data` content, and your local `site.cfg` is not available for
-benchmark scripts.
+benchmark scripts. The benchmark runner defers to any value already set in
+the shell, but will otherwise download `iris-test-data` and set the variable
+accordingly.
 * `DATA_GEN_PYTHON` - required - path to a Python executable that can be
 used to generate benchmark test objects/files; see
 [Data generation](#data-generation). The benchmark runner sets this 
-automatically, but will defer to any value already set in the shell.
+automatically, but will defer to any value already set in the shell. Note that
+[Mule](https://github.com/metomi/mule) will be  automatically installed into 
+this environment, and sometimes 
+[iris-test-data](https://github.com/SciTools/iris-test-data) (see 
+`OVERRIDE_TEST_DATA_REPOSITORY`).
 * `BENCHMARK_DATA` - optional - path to a directory for benchmark synthetic
 test data, which the benchmark scripts will create if it doesn't already
 exist. Defaults to `<root>/benchmarks/.data/` if not set. Note that some of
diff --git a/benchmarks/benchmarks/__init__.py b/benchmarks/benchmarks/__init__.py
index 73f5cd6399..378c26332d 100644
--- a/benchmarks/benchmarks/__init__.py
+++ b/benchmarks/benchmarks/__init__.py
@@ -5,7 +5,9 @@
 """Common code for benchmarks."""
 
 from os import environ
-import resource
+import tracemalloc
+
+import numpy as np
 
 
 def disable_repeat_between_setup(benchmark_object):
@@ -61,27 +63,34 @@ class TrackAddedMemoryAllocation:
         AVD's detection threshold and be treated as 'signal'. Results
         smaller than this value will therefore be returned as equal to this
         value, ensuring fractionally small noise / no noise at all.
+        Defaults to 1.0
+
+    RESULT_ROUND_DP : int
+        Number of decimal places of rounding on result values (in Mb).
+        Defaults to 1
 
     """
 
-    RESULT_MINIMUM_MB = 5.0
-
-    @staticmethod
-    def process_resident_memory_mb():
-        return resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024.0
+    RESULT_MINIMUM_MB = 0.2
+    RESULT_ROUND_DP = 1  # I.E. to nearest 0.1 Mb
 
     def __enter__(self):
-        self.mb_before = self.process_resident_memory_mb()
+        tracemalloc.start()
         return self
 
     def __exit__(self, *_):
-        self.mb_after = self.process_resident_memory_mb()
+        _, peak_mem_bytes = tracemalloc.get_traced_memory()
+        tracemalloc.stop()
+        # Save peak-memory allocation, scaled from bytes to Mb.
+        self._peak_mb = peak_mem_bytes * (2.0**-20)
 
     def addedmem_mb(self):
         """Return measured memory growth, in Mb."""
-        result = self.mb_after - self.mb_before
+        result = self._peak_mb
         # Small results are too vulnerable to noise being interpreted as signal.
         result = max(self.RESULT_MINIMUM_MB, result)
+        # Rounding makes results easier to read.
+        result = np.round(result, self.RESULT_ROUND_DP)
         return result
 
     @staticmethod
@@ -105,6 +114,33 @@ def _wrapper(*args, **kwargs):
         decorated_func.unit = "Mb"
         return _wrapper
 
+    @staticmethod
+    def decorator_repeating(repeats=3):
+        """Benchmark to track growth in resident memory during execution.
+
+        Tracks memory for repeated calls of decorated function.
+
+        Intended for use on ASV ``track_`` benchmarks. Applies the
+        :class:`TrackAddedMemoryAllocation` context manager to the benchmark
+        code, sets the benchmark ``unit`` attribute to ``Mb``.
+
+        """
+
+        def decorator(decorated_func):
+            def _wrapper(*args, **kwargs):
+                assert decorated_func.__name__[:6] == "track_"
+                # Run the decorated benchmark within the added memory context
+                # manager.
+                with TrackAddedMemoryAllocation() as mb:
+                    for _ in range(repeats):
+                        decorated_func(*args, **kwargs)
+                return mb.addedmem_mb()
+
+            decorated_func.unit = "Mb"
+            return _wrapper
+
+        return decorator
+
 
 def on_demand_benchmark(benchmark_object):
     """Disable these benchmark(s) unless ON_DEMAND_BENCHARKS env var is set.
diff --git a/benchmarks/benchmarks/aggregate_collapse.py b/benchmarks/benchmarks/aggregate_collapse.py
new file mode 100644
index 0000000000..4d5d2923bc
--- /dev/null
+++ b/benchmarks/benchmarks/aggregate_collapse.py
@@ -0,0 +1,212 @@
+# Copyright Iris contributors
+#
+# This file is part of Iris and is released under the BSD license.
+# See LICENSE in the root of the repository for full licensing details.
+"""Benchmarks relating to :meth:`iris.cube.CubeList.merge` and ``concatenate``."""
+
+import warnings
+
+import numpy as np
+
+from iris import analysis, coords, cube
+from iris.warnings import IrisVagueMetadataWarning
+
+from .generate_data.stock import realistic_4d_w_everything
+
+
+class AggregationMixin:
+    params = [[False, True]]
+    param_names = ["Lazy operations"]
+
+    def setup(self, lazy_run: bool):
+        warnings.filterwarnings("ignore", message="Ignoring a datum")
+        warnings.filterwarnings("ignore", category=IrisVagueMetadataWarning)
+        cube = realistic_4d_w_everything(lazy=lazy_run)
+
+        for cm in cube.cell_measures():
+            cube.remove_cell_measure(cm)
+        for av in cube.ancillary_variables():
+            cube.remove_ancillary_variable(av)
+
+        agg_mln_data = np.arange(0, 70, 10)
+        agg_mln_repeat = np.repeat(agg_mln_data, 10)
+
+        cube = cube[..., :10, :10]
+
+        self.mln_aux = "aggregatable"
+        self.mln = "model_level_number"
+        agg_mln_coord = coords.AuxCoord(points=agg_mln_repeat, long_name=self.mln_aux)
+
+        if lazy_run:
+            agg_mln_coord.points = agg_mln_coord.lazy_points()
+        cube.add_aux_coord(agg_mln_coord, 1)
+        self.cube = cube
+
+
+class Aggregation(AggregationMixin):
+    def time_aggregated_by_MEAN(self, _):
+        _ = self.cube.aggregated_by(self.mln_aux, analysis.MEAN).data
+
+    def time_aggregated_by_COUNT(self, _):
+        _ = self.cube.aggregated_by(
+            self.mln_aux, analysis.COUNT, function=lambda values: values > 280
+        ).data
+
+    def time_aggregated_by_GMEAN(self, _):
+        _ = self.cube.aggregated_by(self.mln_aux, analysis.GMEAN).data
+
+    def time_aggregated_by_HMEAN(self, _):
+        _ = self.cube.aggregated_by(self.mln_aux, analysis.HMEAN).data
+
+    def time_aggregated_by_MAX_RUN(self, _):
+        _ = self.cube.aggregated_by(
+            self.mln_aux, analysis.MAX_RUN, function=lambda values: values > 280
+        ).data
+
+    def time_aggregated_by_MAX(self, _):
+        _ = self.cube.aggregated_by(self.mln_aux, analysis.MAX).data
+
+    def time_aggregated_by_MEDIAN(self, _):
+        _ = self.cube.aggregated_by(self.mln_aux, analysis.MEDIAN).data
+
+    def time_aggregated_by_MIN(self, _):
+        _ = self.cube.aggregated_by(self.mln_aux, analysis.MIN).data
+
+    def time_aggregated_by_PEAK(self, _):
+        _ = self.cube.aggregated_by(self.mln_aux, analysis.PEAK).data
+
+    def time_aggregated_by_PERCENTILE(self, _):
+        _ = self.cube.aggregated_by(
+            self.mln_aux, analysis.PERCENTILE, percent=[10, 50, 90]
+        ).data
+
+    def time_aggregated_by_FAST_PERCENTILE(self, _):
+        _ = self.cube.aggregated_by(
+            self.mln_aux,
+            analysis.PERCENTILE,
+            mdtol=0,
+            percent=[10, 50, 90],
+            fast_percentile_method=True,
+        ).data
+
+    def time_aggregated_by_PROPORTION(self, _):
+        _ = self.cube.aggregated_by(
+            self.mln_aux,
+            analysis.PROPORTION,
+            function=lambda values: values > 280,
+        ).data
+
+    def time_aggregated_by_STD_DEV(self, _):
+        _ = self.cube.aggregated_by(self.mln_aux, analysis.STD_DEV).data
+
+    def time_aggregated_by_VARIANCE(self, _):
+        _ = self.cube.aggregated_by(self.mln_aux, analysis.VARIANCE).data
+
+    def time_aggregated_by_RMS(self, _):
+        _ = self.cube.aggregated_by(self.mln_aux, analysis.RMS).data
+
+    def time_collapsed_by_MEAN(self, _):
+        _ = self.cube.collapsed(self.mln, analysis.MEAN).data
+
+    def time_collapsed_by_COUNT(self, _):
+        _ = self.cube.collapsed(
+            self.mln, analysis.COUNT, function=lambda values: values > 280
+        ).data
+
+    def time_collapsed_by_GMEAN(self, _):
+        _ = self.cube.collapsed(self.mln, analysis.GMEAN).data
+
+    def time_collapsed_by_HMEAN(self, _):
+        _ = self.cube.collapsed(self.mln, analysis.HMEAN).data
+
+    def time_collapsed_by_MAX_RUN(self, _):
+        _ = self.cube.collapsed(
+            self.mln, analysis.MAX_RUN, function=lambda values: values > 280
+        ).data
+
+    def time_collapsed_by_MAX(self, _):
+        _ = self.cube.collapsed(self.mln, analysis.MAX).data
+
+    def time_collapsed_by_MEDIAN(self, _):
+        _ = self.cube.collapsed(self.mln, analysis.MEDIAN).data
+
+    def time_collapsed_by_MIN(self, _):
+        _ = self.cube.collapsed(self.mln, analysis.MIN).data
+
+    def time_collapsed_by_PEAK(self, _):
+        _ = self.cube.collapsed(self.mln, analysis.PEAK).data
+
+    def time_collapsed_by_PERCENTILE(self, _):
+        _ = self.cube.collapsed(
+            self.mln, analysis.PERCENTILE, percent=[10, 50, 90]
+        ).data
+
+    def time_collapsed_by_FAST_PERCENTILE(self, _):
+        _ = self.cube.collapsed(
+            self.mln,
+            analysis.PERCENTILE,
+            mdtol=0,
+            percent=[10, 50, 90],
+            fast_percentile_method=True,
+        ).data
+
+    def time_collapsed_by_PROPORTION(self, _):
+        _ = self.cube.collapsed(
+            self.mln, analysis.PROPORTION, function=lambda values: values > 280
+        ).data
+
+    def time_collapsed_by_STD_DEV(self, _):
+        _ = self.cube.collapsed(self.mln, analysis.STD_DEV).data
+
+    def time_collapsed_by_VARIANCE(self, _):
+        _ = self.cube.collapsed(self.mln, analysis.VARIANCE).data
+
+    def time_collapsed_by_RMS(self, _):
+        _ = self.cube.collapsed(self.mln, analysis.RMS).data
+
+
+class WeightedAggregation(AggregationMixin):
+    def setup(self, lazy_run):
+        super().setup(lazy_run)
+
+        weights = np.linspace(0, 1, 70)
+        weights = np.broadcast_to(weights, self.cube.shape[:2])
+        weights = np.broadcast_to(weights.T, self.cube.shape[::-1])
+        weights = weights.T
+
+        self.weights = weights
+
+    ## currently has problems with indexing weights
+    # def time_w_aggregated_by_WPERCENTILE(self, _):
+    #     _ = self.cube.aggregated_by(
+    #         self.mln_aux, analysis.WPERCENTILE, weights=self.weights, percent=[10, 50, 90]
+    #     ).data
+
+    def time_w_aggregated_by_SUM(self, _):
+        _ = self.cube.aggregated_by(
+            self.mln_aux, analysis.SUM, weights=self.weights
+        ).data
+
+    def time_w_aggregated_by_RMS(self, _):
+        _ = self.cube.aggregated_by(
+            self.mln_aux, analysis.RMS, weights=self.weights
+        ).data
+
+    def time_w_aggregated_by_MEAN(self, _):
+        _ = self.cube.aggregated_by(
+            self.mln_aux, analysis.MEAN, weights=self.weights
+        ).data
+
+    def time_w_collapsed_by_WPERCENTILE(self, _):
+        _ = self.cube.collapsed(
+            self.mln, analysis.WPERCENTILE, weights=self.weights, percent=[10, 50, 90]
+        ).data
+
+    def time_w_collapsed_by_SUM(self, _):
+        _ = self.cube.collapsed(self.mln, analysis.SUM, weights=self.weights).data
+
+    def time_w_collapsed_by_RMS(self, _):
+        _ = self.cube.collapsed(self.mln, analysis.RMS, weights=self.weights).data
+
+    def time_w_collapsed_by_MEAN(self, _):
+        _ = self.cube.collapsed(self.mln, analysis.MEAN, weights=self.weights).data
diff --git a/benchmarks/benchmarks/experimental/ugrid/regions_combine.py b/benchmarks/benchmarks/experimental/ugrid/regions_combine.py
index 409d6961c3..e74b2f2066 100644
--- a/benchmarks/benchmarks/experimental/ugrid/regions_combine.py
+++ b/benchmarks/benchmarks/experimental/ugrid/regions_combine.py
@@ -18,7 +18,7 @@
 from iris.experimental.ugrid import PARSE_UGRID_ON_LOAD
 from iris.experimental.ugrid.utils import recombine_submeshes
 
-from ... import TrackAddedMemoryAllocation, on_demand_benchmark
+from ... import TrackAddedMemoryAllocation
 from ...generate_data.ugrid import make_cube_like_2d_cubesphere
 
 
@@ -182,8 +182,6 @@ class CombineRegionsComputeRealData(MixinCombineRegions):
     def time_compute_data(self, n_cubesphere):
         _ = self.recombined_cube.data
 
-    # Vulnerable to noise, so disabled by default.
-    @on_demand_benchmark
     @TrackAddedMemoryAllocation.decorator
     def track_addedmem_compute_data(self, n_cubesphere):
         _ = self.recombined_cube.data
@@ -203,8 +201,6 @@ def time_save(self, n_cubesphere):
         # Save to disk, which must compute data + stream it to file.
         save(self.recombined_cube, "tmp.nc")
 
-    # Vulnerable to noise, so disabled by default.
-    @on_demand_benchmark
     @TrackAddedMemoryAllocation.decorator
     def track_addedmem_save(self, n_cubesphere):
         save(self.recombined_cube, "tmp.nc")
@@ -233,8 +229,6 @@ def time_stream_file2file(self, n_cubesphere):
         # Save to disk, which must compute data + stream it to file.
         save(self.recombined_cube, "tmp.nc")
 
-    # Vulnerable to noise, so disabled by default.
-    @on_demand_benchmark
     @TrackAddedMemoryAllocation.decorator
     def track_addedmem_stream_file2file(self, n_cubesphere):
         save(self.recombined_cube, "tmp.nc")
diff --git a/benchmarks/benchmarks/generate_data/stock.py b/benchmarks/benchmarks/generate_data/stock.py
index 8b29597629..61f085195a 100644
--- a/benchmarks/benchmarks/generate_data/stock.py
+++ b/benchmarks/benchmarks/generate_data/stock.py
@@ -13,6 +13,7 @@
 from pathlib import Path
 
 import iris
+from iris import cube
 from iris.experimental.ugrid import PARSE_UGRID_ON_LOAD, load_mesh
 
 from . import BENCHMARK_DATA, REUSE_DATA, load_realised, run_function_elsewhere
@@ -153,7 +154,7 @@ def _external(sample_mesh_kwargs_, save_path_):
     return source_mesh.to_MeshCoord(location=location, axis=axis)
 
 
-def realistic_4d_w_everything(w_mesh=False, lazy=False):
+def realistic_4d_w_everything(w_mesh=False, lazy=False) -> iris.cube.Cube:
     """Run :func:`iris.tests.stock.realistic_4d_w_everything` in ``DATA_GEN_PYTHON``.
 
     Parameters
diff --git a/benchmarks/benchmarks/merge_concat.py b/benchmarks/benchmarks/merge_concat.py
index 315d90f575..1a18f92ce9 100644
--- a/benchmarks/benchmarks/merge_concat.py
+++ b/benchmarks/benchmarks/merge_concat.py
@@ -8,6 +8,7 @@
 
 from iris.cube import CubeList
 
+from . import TrackAddedMemoryAllocation
 from .generate_data.stock import realistic_4d_w_everything
 
 
@@ -33,6 +34,10 @@ def setup(self):
     def time_merge(self):
         _ = self.cube_list.merge_cube()
 
+    @TrackAddedMemoryAllocation.decorator_repeating()
+    def track_mem_merge(self):
+        _ = self.cube_list.merge_cube()
+
 
 class Concatenate:
     # TODO: Improve coverage.
@@ -50,3 +55,7 @@ def setup(self):
 
     def time_concatenate(self):
         _ = self.cube_list.concatenate_cube()
+
+    @TrackAddedMemoryAllocation.decorator_repeating()
+    def track_mem_merge(self):
+        _ = self.cube_list.concatenate_cube()
diff --git a/benchmarks/benchmarks/regridding.py b/benchmarks/benchmarks/regridding.py
index b311c94717..4cfda05ad1 100644
--- a/benchmarks/benchmarks/regridding.py
+++ b/benchmarks/benchmarks/regridding.py
@@ -14,6 +14,8 @@
 from iris.analysis import AreaWeighted, PointInCell
 from iris.coords import AuxCoord
 
+from . import TrackAddedMemoryAllocation
+
 
 class HorizontalChunkedRegridding:
     def setup(self) -> None:
@@ -51,6 +53,20 @@ def time_regrid_area_w_new_grid(self) -> None:
         # Realise data
         out.data
 
+    @TrackAddedMemoryAllocation.decorator_repeating()
+    def track_mem_regrid_area_w(self) -> None:
+        # Regrid the chunked cube
+        out = self.cube.regrid(self.template_cube, self.scheme_area_w)
+        # Realise data
+        out.data
+
+    @TrackAddedMemoryAllocation.decorator_repeating()
+    def track_mem_regrid_area_w_new_grid(self) -> None:
+        # Regrid the chunked cube
+        out = self.chunked_cube.regrid(self.template_cube, self.scheme_area_w)
+        # Realise data
+        out.data
+
 
 class CurvilinearRegridding:
     def setup(self) -> None:
@@ -93,3 +109,10 @@ def time_regrid_pic(self) -> None:
         out = self.cube.regrid(self.template_cube, self.scheme_pic)
         # Realise the data
         out.data
+
+    @TrackAddedMemoryAllocation.decorator_repeating()
+    def track_mem_regrid_pic(self) -> None:
+        # Regrid the cube onto the template.
+        out = self.cube.regrid(self.template_cube, self.scheme_pic)
+        # Realise the data
+        out.data
diff --git a/benchmarks/benchmarks/save.py b/benchmarks/benchmarks/save.py
index 9936331621..f78734835d 100644
--- a/benchmarks/benchmarks/save.py
+++ b/benchmarks/benchmarks/save.py
@@ -38,8 +38,6 @@ def time_netcdf_save_mesh(self, n_cubesphere, is_unstructured):
         if is_unstructured:
             self._save_mesh(self.cube)
 
-    # Vulnerable to noise, so disabled by default.
-    @on_demand_benchmark
     @TrackAddedMemoryAllocation.decorator
     def track_addedmem_netcdf_save(self, n_cubesphere, is_unstructured):
         # Don't need to copy the cube here since track_ benchmarks don't
diff --git a/benchmarks/benchmarks/stats.py b/benchmarks/benchmarks/stats.py
index 0530431900..1f5262bf4c 100644
--- a/benchmarks/benchmarks/stats.py
+++ b/benchmarks/benchmarks/stats.py
@@ -8,6 +8,8 @@
 from iris.analysis.stats import pearsonr
 import iris.tests
 
+from . import TrackAddedMemoryAllocation
+
 
 class PearsonR:
     def setup(self):
@@ -30,9 +32,21 @@ def setup(self):
     def time_real(self):
         pearsonr(self.cube_a, self.cube_b, weights=self.weights)
 
+    @TrackAddedMemoryAllocation.decorator_repeating()
+    def track_real(self):
+        pearsonr(self.cube_a, self.cube_b, weights=self.weights)
+
     def time_lazy(self):
         for cube in self.cube_a, self.cube_b:
             cube.data = cube.lazy_data()
 
         result = pearsonr(self.cube_a, self.cube_b, weights=self.weights)
         result.data
+
+    @TrackAddedMemoryAllocation.decorator_repeating()
+    def track_lazy(self):
+        for cube in self.cube_a, self.cube_b:
+            cube.data = cube.lazy_data()
+
+        result = pearsonr(self.cube_a, self.cube_b, weights=self.weights)
+        result.data
diff --git a/benchmarks/benchmarks/trajectory.py b/benchmarks/benchmarks/trajectory.py
index ec2958b6a8..a31552eb9a 100644
--- a/benchmarks/benchmarks/trajectory.py
+++ b/benchmarks/benchmarks/trajectory.py
@@ -13,6 +13,8 @@
 import iris
 from iris.analysis.trajectory import interpolate
 
+from . import TrackAddedMemoryAllocation
+
 
 class TrajectoryInterpolation:
     def setup(self) -> None:
@@ -33,8 +35,22 @@ def time_trajectory_linear(self) -> None:
         # Realise the data
         out_cube.data
 
+    @TrackAddedMemoryAllocation.decorator_repeating()
+    def track_trajectory_linear(self) -> None:
+        # Regrid the cube onto the template.
+        out_cube = interpolate(self.cube, self.sample_points, method="linear")
+        # Realise the data
+        out_cube.data
+
     def time_trajectory_nearest(self) -> None:
         # Regrid the cube onto the template.
         out_cube = interpolate(self.cube, self.sample_points, method="nearest")
         # Realise the data
         out_cube.data
+
+    @TrackAddedMemoryAllocation.decorator_repeating()
+    def track_trajectory_nearest(self) -> None:
+        # Regrid the cube onto the template.
+        out_cube = interpolate(self.cube, self.sample_points, method="nearest")
+        # Realise the data
+        out_cube.data
diff --git a/benchmarks/bm_runner.py b/benchmarks/bm_runner.py
index 2c18de4a41..770ef737b4 100644
--- a/benchmarks/bm_runner.py
+++ b/benchmarks/bm_runner.py
@@ -91,17 +91,16 @@ def _prep_data_gen_env() -> None:
         ).resolve()
         environ[data_gen_var] = str(data_gen_python)
 
+        def clone_resource(name: str, clone_source: str) -> Path:
+            resource_dir = data_gen_python.parents[1] / "resources"
+            resource_dir.mkdir(exist_ok=True)
+            clone_dir = resource_dir / name
+            if not clone_dir.is_dir():
+                _subprocess_runner(["git", "clone", clone_source, str(clone_dir)])
+            return clone_dir
+
         echo("Installing Mule into data generation environment ...")
-        mule_dir = data_gen_python.parents[1] / "resources" / "mule"
-        if not mule_dir.is_dir():
-            _subprocess_runner(
-                [
-                    "git",
-                    "clone",
-                    "https://github.com/metomi/mule.git",
-                    str(mule_dir),
-                ]
-            )
+        mule_dir = clone_resource("mule", "https://github.com/metomi/mule.git")
         _subprocess_runner(
             [
                 str(data_gen_python),
@@ -112,6 +111,14 @@ def _prep_data_gen_env() -> None:
             ]
         )
 
+        test_data_var = "OVERRIDE_TEST_DATA_REPOSITORY"
+        if test_data_var not in environ:
+            echo("Installing iris-test-data into data generation environment ...")
+            test_data_dir = clone_resource(
+                "iris-test-data", "https://github.com/SciTools/iris-test-data.git"
+            )
+            environ[test_data_var] = str(test_data_dir / "test_data")
+
         echo("Data generation environment ready.")
 
 
@@ -537,6 +544,63 @@ def func(args: argparse.Namespace) -> None:
         _subprocess_runner([args.asv_sub_command, *args.asv_args], asv=True)
 
 
+class TrialRun(_SubParserGenerator):
+    name = "trialrun"
+    description = (
+        "Fast trial-run a given benchmark, to check it works : "
+        "in a provided or latest-lockfile environment, "
+        "with no repeats for accuracy of measurement."
+    )
+    epilog = (
+        "e.g. python bm_runner.py trialrun "
+        "MyBenchmarks.time_calc ${DATA_GEN_PYTHON}"
+        "\n\nNOTE: 'runpath' also replaces $DATA_GEN_PYTHON during the run."
+    )
+
+    def add_arguments(self) -> None:
+        self.subparser.add_argument(
+            "benchmark",
+            type=str,
+            help=(
+                "A benchmark name, possibly including wildcards, "
+                "as supported by the ASV '--bench' argument."
+            ),
+        )
+        self.subparser.add_argument(
+            "runpath",
+            type=str,
+            help=(
+                "A path to an existing python executable, "
+                "to completely bypass environment building."
+            ),
+        )
+
+    @staticmethod
+    def func(args: argparse.Namespace) -> None:
+        if args.runpath:
+            # Shortcut creation of a data-gen environment
+            # - which is also the trial-run env.
+            python_path = Path(args.runpath).resolve()
+            environ["DATA_GEN_PYTHON"] = str(python_path)
+        _setup_common()
+        # get path of data-gen environment, setup by previous call
+        python_path = environ["DATA_GEN_PYTHON"]
+        # allow 'on-demand' benchmarks
+        environ["ON_DEMAND_BENCHMARKS"] = "1"
+        asv_command = [
+            "run",
+            "--bench",
+            args.benchmark,
+            # no repeats for timing accuracy
+            "--quick",
+            "--show-stderr",
+            # do not build a unique env : run test in data-gen environment
+            "--environment",
+            f"existing:{python_path}",
+        ] + args.asv_args
+        _subprocess_runner(asv_command, asv=True)
+
+
 class GhPost(_SubParserGenerator):
     name = "_gh_post"
     description = (
@@ -562,11 +626,24 @@ def add_asv_arguments(self) -> None:
 def main():
     parser = ArgumentParser(
         description="Run the Iris performance benchmarks (using Airspeed Velocity).",
-        epilog="More help is available within each sub-command.",
+        epilog=(
+            "More help is available within each sub-command."
+            "\n\nNOTE(1): a separate python environment is created to "
+            "construct test files.\n   Set $DATA_GEN_PYTHON to avoid the cost "
+            "of this."
+            "\nNOTE(2): iris-test-data is downloaded and cached within the "
+            "data generation environment.\n   Set "
+            "$OVERRIDE_TEST_DATA_REPOSITORY to avoid the cost of this."
+            "\nNOTE(3): test data is cached within the "
+            "benchmarks code directory, and uses a lot of disk space "
+            "of disk space (Gb).\n   Set $BENCHMARK_DATA to specify where this "
+            "space can be safely allocated."
+        ),
+        formatter_class=argparse.RawTextHelpFormatter,
     )
     subparsers = parser.add_subparsers(required=True)
 
-    for gen in (Overnight, Branch, CPerf, SPerf, Custom, GhPost):
+    for gen in (Overnight, Branch, CPerf, SPerf, Custom, TrialRun, GhPost):
         _ = gen(subparsers).subparser
 
     parsed = parser.parse_args()
diff --git a/docs/src/conf.py b/docs/src/conf.py
index 2534cd5c02..60f760c37f 100644
--- a/docs/src/conf.py
+++ b/docs/src/conf.py
@@ -21,12 +21,14 @@
 
 import datetime
 from importlib.metadata import version as get_version
+from inspect import getsource
 import ntpath
 import os
 from pathlib import Path
 import re
 from subprocess import run
 import sys
+from tempfile import gettempdir
 from urllib.parse import quote
 import warnings
 
@@ -409,6 +411,26 @@ def _dotv(version):
 # -- sphinx-gallery config ----------------------------------------------------
 # See https://sphinx-gallery.github.io/stable/configuration.html
 
+
+def reset_modules(gallery_conf, fname):
+    """Force re-registering of nc-time-axis with matplotlib for each example.
+
+    Required for sphinx-gallery>=0.11.0.
+    """
+    from sys import modules
+
+    _ = modules.pop("nc_time_axis", None)
+
+
+# https://sphinx-gallery.github.io/dev/configuration.html#importing-callables
+reset_modules_dir = Path(gettempdir()) / reset_modules.__name__
+reset_modules_dir.mkdir(exist_ok=True)
+(reset_modules_dir / f"{reset_modules.__name__}.py").write_text(
+    getsource(reset_modules)
+)
+sys.path.insert(0, str(reset_modules_dir))
+
+
 sphinx_gallery_conf = {
     # path to your example scripts
     "examples_dirs": ["../gallery_code"],
@@ -420,11 +442,7 @@ def _dotv(version):
     "ignore_pattern": r"__init__\.py",
     # force gallery building, unless overridden (see src/Makefile)
     "plot_gallery": "'True'",
-    # force re-registering of nc-time-axis with matplotlib for each example,
-    # required for sphinx-gallery>=0.11.0
-    "reset_modules": (
-        lambda gallery_conf, fname: sys.modules.pop("nc_time_axis", None),
-    ),
+    "reset_modules": f"{reset_modules.__name__}.{reset_modules.__name__}",
 }
 
 # -----------------------------------------------------------------------------
diff --git a/docs/src/copyright.rst b/docs/src/copyright.rst
index 513d281c07..b0d68cfe8c 100644
--- a/docs/src/copyright.rst
+++ b/docs/src/copyright.rst
@@ -27,10 +27,10 @@ are licensed under the UK's Open Government Licence:
 
     (C) British Crown Copyright |copyright_years|
 
-    You may use and re-use the information featured on this website (not including logos) free of
+    You may use and reuse the information featured on this website (not including logos) free of
     charge in any format or medium, under the terms of the
     `Open Government Licence <https://www.nationalarchives.gov.uk/doc/open-government-licence>`_.
     We encourage users to establish hypertext links to this website.
 
-    Any email enquiries regarding the use and re-use of this information resource should be
+    Any email enquiries regarding the use and reuse of this information resource should be
     sent to: psi@nationalarchives.gsi.gov.uk.
diff --git a/docs/src/developers_guide/contributing_benchmarks.rst b/docs/src/developers_guide/contributing_benchmarks.rst
index b6d005df68..ccb9a50e39 100644
--- a/docs/src/developers_guide/contributing_benchmarks.rst
+++ b/docs/src/developers_guide/contributing_benchmarks.rst
@@ -7,8 +7,9 @@ Benchmarking
 Iris includes architecture for benchmarking performance and other metrics of
 interest. This is done using the `Airspeed Velocity`_ (ASV) package.
 
-Full detail on the setup and how to run or write benchmarks is in
-`benchmarks/README.md`_ in the Iris repository.
+
+.. note:: Full detail on the setup and how to run or write benchmarks is in
+          `benchmarks/README.md`_ in the Iris repository.
 
 Continuous Integration
 ----------------------
diff --git a/docs/src/further_topics/dask_best_practices/dask_bags_and_greed.rst b/docs/src/further_topics/dask_best_practices/dask_bags_and_greed.rst
index 007a58d400..272ea6fc08 100644
--- a/docs/src/further_topics/dask_best_practices/dask_bags_and_greed.rst
+++ b/docs/src/further_topics/dask_best_practices/dask_bags_and_greed.rst
@@ -7,7 +7,7 @@ Here is a journey that demonstrates:
 
 * How to apply dask.bags to an existing script
 * The equal importance of optimisation of non-parallel parts of a script
-* Protection against multiple softwares trying to manage parallelism
+* Protection against multiple software trying to manage parallelism
   simultaneously
 
 
diff --git a/docs/src/further_topics/netcdf_io.rst b/docs/src/further_topics/netcdf_io.rst
index bae32ebcae..4e1c32b22f 100644
--- a/docs/src/further_topics/netcdf_io.rst
+++ b/docs/src/further_topics/netcdf_io.rst
@@ -66,7 +66,7 @@ creation of the :data:`iris.fileformats.netcdf.loader.CHUNK_CONTROL` class.
 Custom Chunking: Set
 ^^^^^^^^^^^^^^^^^^^^
 
-There are three context manangers within :data:`~iris.fileformats.netcdf.loader.CHUNK_CONTROL`. The most basic is
+There are three context managers within :data:`~iris.fileformats.netcdf.loader.CHUNK_CONTROL`. The most basic is
 :meth:`~iris.fileformats.netcdf.loader.ChunkControl.set`. This allows you to specify the chunksize for each dimension,
 and to specify a ``var_name`` specifically to change.
 
diff --git a/docs/src/further_topics/ugrid/partner_packages.rst b/docs/src/further_topics/ugrid/partner_packages.rst
index 75b54b037f..1c7a8b8c8c 100644
--- a/docs/src/further_topics/ugrid/partner_packages.rst
+++ b/docs/src/further_topics/ugrid/partner_packages.rst
@@ -93,7 +93,7 @@ Applications
 * Regrid unstructured to structured.
 * Regrid with dask integration, computing in parallel and maintaining data
   laziness.
-* | Save a prepared regridder for re-use in subsequent runs.
+* | Save a prepared regridder for reuse in subsequent runs.
   | Regridders can even be re-used on sources with different masks - a
     significant efficiency gain.
 
diff --git a/docs/src/further_topics/which_regridder_to_use.rst b/docs/src/further_topics/which_regridder_to_use.rst
index 5d7d7fdba1..dae273252d 100644
--- a/docs/src/further_topics/which_regridder_to_use.rst
+++ b/docs/src/further_topics/which_regridder_to_use.rst
@@ -341,7 +341,7 @@ of ``kg m-2`` as an area weighted sum. With ``mdtol=0`` this will consistently
 underestimate this total and with ``mdtol=1`` will consistently overestimate. This can
 be somewhat mitigated with a choice of ``mdtol=0.5``, but you should still be aware of
 potential inaccuracies. It should be noted that this choice of ``mdtol`` is highly
-context dependent and there wil likely be occasions where a choice of ``mdtol=0`` or
+context dependent and there will likely be occasions where a choice of ``mdtol=0`` or
 ``mdtol=1`` is more suitable. The important thing is to *know your data, know what*
 *you're doing with your data and know how your regridder fits in this process*.
 
diff --git a/docs/src/whatsnew/3.6.rst b/docs/src/whatsnew/3.6.rst
index 389356df46..b5a23ac401 100644
--- a/docs/src/whatsnew/3.6.rst
+++ b/docs/src/whatsnew/3.6.rst
@@ -15,11 +15,11 @@ This document explains the changes made to Iris for this release
 
     We're so excited about our recent support for **delayed saving of lazy data
     to netCDF** (:pull:`5191`) that we're celebrating this important step change
-    in behavour with its very own dedicated release 🥳
+    in behaviour with its very own dedicated release 🥳
 
     By using ``iris.save(..., compute=False)`` you can now save to multiple NetCDF files
     in parallel.  See the new ``compute`` keyword in :func:`iris.fileformats.netcdf.save`.
-    This can share and re-use any common (lazy) result computations, and it makes much
+    This can share and reuse any common (lazy) result computations, and it makes much
     better use of resources during any file-system waiting (i.e., it can use such periods
     to progress the *other* saves).
 
diff --git a/docs/src/whatsnew/latest.rst b/docs/src/whatsnew/latest.rst
index 206bf0ba9e..fb8098f766 100644
--- a/docs/src/whatsnew/latest.rst
+++ b/docs/src/whatsnew/latest.rst
@@ -55,14 +55,14 @@ This document explains the changes made to Iris for this release
 🚀 Performance Enhancements
 ===========================
 
-#. N/A
-
 #. `@bouweandela`_ added the option to specify the Dask chunks of the target
    array in :func:`iris.util.broadcast_to_shape`. (:pull:`5620`)
 
 #. `@schlunma`_ allowed :func:`iris.analysis.cartography.area_weights` to
    return dask arrays with arbitrary chunks. (:pull:`5658`)
 
+#. `@bouweandela`_ made :meth:`iris.cube.Cube.rolling_window` work with lazy
+   data. (:pull:`5795`)
 
 🔥 Deprecations
 ===============
@@ -102,6 +102,30 @@ This document explains the changes made to Iris for this release
    benchmark runs, and introduced larger more 'real world' benchmarks where
    coverage was needed. (:pull:`5949`).
 
+#. `@trexfeathers`_ made a Nox `benchmarks` session as the recommended entry
+   point for running benchmarks. (:pull:`5951`)
+
+#. `@ESadek-MO`_ added further `benchmarks` for aggregation and collapse.
+   (:pull:`5954`)
+
+#. `@trexfeathers`_ set the benchmark data generation environment to
+   automatically install iris-test-data during setup. (:pull:`5958`)
+
+#. `@pp-mo`_ reworked benchmark peak-memory measurement to use the
+   `tracemalloc <https://docs.python.org/3.12/library/tracemalloc.html>`_
+   package.
+   (:pull:`5948`)
+
+#. `@pp-mo`_ added a benchmark 'trialrun' sub-command, to quickly test
+   benchmarks during development. (:pull:`5957`)
+
+#. `@pp-mo`_ moved several memory-measurement benchmarks from 'on-demand' to
+   the standard set, in hopes that use of 'tracemalloc' (:pull:`5948`) makes
+   the results consistent enough to monitor for performance changes.
+   (:pull:`5959`)
+
+#. `@rcomer`_ made some :meth:`~iris.cube.Cube.slices_over` tests go faster (:pull:`5973`)
+
 
 .. comment
     Whatsnew author names (@github name) in alphabetical order. Note that,
diff --git a/lib/iris/analysis/calculus.py b/lib/iris/analysis/calculus.py
index 75b7db050b..4cb634efbe 100644
--- a/lib/iris/analysis/calculus.py
+++ b/lib/iris/analysis/calculus.py
@@ -760,7 +760,7 @@ def spatial_vectors_with_phenom_name(i_cube, j_cube, k_cube=None):
     The cube standard names must match one of the combinations in
     :data:`DIRECTIONAL_NAMES`.
 
-    This routine is designed to identify the vector quantites which each
+    This routine is designed to identify the vector quantities which each
     of the cubes provided represent and return a list of their 3d
     spatial dimension names and associated phenomenon.
     For example, given a cube of "u wind" and "v wind" the return value
diff --git a/lib/iris/cube.py b/lib/iris/cube.py
index 8418a630b5..dc8a08e1bd 100644
--- a/lib/iris/cube.py
+++ b/lib/iris/cube.py
@@ -4552,12 +4552,6 @@ def rolling_window(self, coord, aggregator, window, **kwargs):
         -------
         :class:`iris.cube.Cube`.
 
-        Notes
-        -----
-        .. note::
-
-            This operation does not yet have support for lazy evaluation.
-
         Examples
         --------
             >>> import iris, iris.analysis
@@ -4661,7 +4655,7 @@ def rolling_window(self, coord, aggregator, window, **kwargs):
         # this will add an extra dimension to the data at dimension + 1 which
         # represents the rolled window (i.e. will have a length of window)
         rolling_window_data = iris.util.rolling_window(
-            self.data, window=window, axis=dimension
+            self.core_data(), window=window, axis=dimension
         )
 
         # now update all of the coordinates to reflect the aggregation
@@ -4680,7 +4674,7 @@ def rolling_window(self, coord, aggregator, window, **kwargs):
                     "coordinate." % coord_.name()
                 )
 
-            new_bounds = iris.util.rolling_window(coord_.points, window)
+            new_bounds = iris.util.rolling_window(coord_.core_points(), window)
 
             if np.issubdtype(new_bounds.dtype, np.str_):
                 # Handle case where the AuxCoord contains string. The points
@@ -4726,9 +4720,12 @@ def rolling_window(self, coord, aggregator, window, **kwargs):
                 kwargs["weights"] = iris.util.broadcast_to_shape(
                     weights, rolling_window_data.shape, (dimension + 1,)
                 )
-        data_result = aggregator.aggregate(
-            rolling_window_data, axis=dimension + 1, **kwargs
-        )
+
+        if aggregator.lazy_func is not None and self.has_lazy_data():
+            agg_method = aggregator.lazy_aggregate
+        else:
+            agg_method = aggregator.aggregate
+        data_result = agg_method(rolling_window_data, axis=dimension + 1, **kwargs)
         result = aggregator.post_process(new_cube, data_result, [coord], **kwargs)
         return result
 
diff --git a/lib/iris/fileformats/netcdf/saver.py b/lib/iris/fileformats/netcdf/saver.py
index 8d53a4d5be..676eb69631 100644
--- a/lib/iris/fileformats/netcdf/saver.py
+++ b/lib/iris/fileformats/netcdf/saver.py
@@ -458,7 +458,7 @@ def write(
         cube : :class:`iris.cube.Cube`
             A :class:`iris.cube.Cube` to be saved to a netCDF file.
         local_keys : iterable of str, optional
-            An interable of cube attribute keys. Any cube attributes with
+            An iterable of cube attribute keys. Any cube attributes with
             matching keys will become attributes on the data variable rather
             than global attributes.
 
@@ -2441,7 +2441,7 @@ def save(
         Underlying netCDF file format, one of 'NETCDF4', 'NETCDF4_CLASSIC',
         'NETCDF3_CLASSIC' or 'NETCDF3_64BIT'. Default is 'NETCDF4' format.
     local_keys : iterable of str, optional
-        An interable of cube attribute keys. Any cube attributes with
+        An iterable of cube attribute keys. Any cube attributes with
         matching keys will become attributes on the data variable rather
         than global attributes.
 
diff --git a/lib/iris/plot.py b/lib/iris/plot.py
index a0c5f55274..ec621f43d4 100644
--- a/lib/iris/plot.py
+++ b/lib/iris/plot.py
@@ -43,6 +43,23 @@
 PlotDefn = collections.namedtuple("PlotDefn", ("coords", "transpose"))
 
 
+class _GeoAxesPatched(cartopy.mpl.geoaxes.GeoAxes):
+    # TODO: see cartopy#2390
+    #  Remove this once the bug is addressed in a Cartopy release.
+    def _draw_preprocess(self, renderer):
+        super()._draw_preprocess(renderer)
+
+        for artist in self.artists:
+            if hasattr(artist, "_draw_gridliner"):
+                # Note this is only necessary since Cartopy v0.23, but is not
+                #  wasteful for earlier versions as _draw_gridliner() includes
+                #  a check for whether a draw is necessary.
+                artist._draw_gridliner(renderer=renderer)
+
+
+cartopy.mpl.geoaxes.GeoAxes = _GeoAxesPatched
+
+
 def _get_plot_defn_custom_coords_picked(cube, coords, mode, ndims=2):
     def names(coords):
         result = []
diff --git a/lib/iris/tests/unit/cube/test_Cube.py b/lib/iris/tests/unit/cube/test_Cube.py
index ec94e346b2..8ae42448c7 100644
--- a/lib/iris/tests/unit/cube/test_Cube.py
+++ b/lib/iris/tests/unit/cube/test_Cube.py
@@ -877,7 +877,7 @@ def setUp(self):
         self.cell_measure = CellMeasure([0, 1, 2, 0, 1, 2], long_name="bar")
         self.multi_dim_cube.add_cell_measure(self.cell_measure, 1)
 
-        self.mock_agg = mock.Mock(spec=Aggregator)
+        self.mock_agg = mock.Mock(spec=Aggregator, lazy_func=None)
         self.mock_agg.aggregate = mock.Mock(return_value=np.empty([4]))
         self.mock_agg.post_process = mock.Mock(side_effect=lambda x, y, z: x)
 
@@ -919,6 +919,21 @@ def test_kwargs(self):
         )
         self.assertMaskedArrayEqual(expected_result, res_cube.data)
 
+    def test_lazy(self):
+        window = 2
+        self.cube.data = da.ma.masked_array(
+            self.cube.data, mask=([True, False, False, False, True, False])
+        )
+        res_cube = self.cube.rolling_window("val", iris.analysis.MEAN, window, mdtol=0)
+        self.assertTrue(self.cube.has_lazy_data())
+        self.assertTrue(res_cube.has_lazy_data())
+        expected_result = ma.array(
+            [-99.0, 1.5, 2.5, -99.0, -99.0],
+            mask=[True, False, False, True, True],
+            dtype=np.float64,
+        )
+        self.assertMaskedArrayEqual(expected_result, res_cube.data)
+
     def test_ancillary_variables_and_cell_measures_kept(self):
         res_cube = self.multi_dim_cube.rolling_window("val", self.mock_agg, 3)
         self.assertEqual(res_cube.ancillary_variables(), [self.ancillary_variable])
@@ -1036,10 +1051,10 @@ def test_all_permutations(self):
 @tests.skip_data
 class Test_slices_over(tests.IrisTest):
     def setUp(self):
-        self.cube = stock.realistic_4d()
+        self.cube = stock.realistic_4d()[:, :7, :10, :10]
         # Define expected iterators for 1D and 2D test cases.
         self.exp_iter_1d = range(len(self.cube.coord("model_level_number").points))
-        self.exp_iter_2d = np.ndindex(6, 70, 1, 1)
+        self.exp_iter_2d = np.ndindex(6, 7, 1, 1)
         # Define maximum number of interactions for particularly long
         # (and so time-consuming) iterators.
         self.long_iterator_max = 5
@@ -1075,7 +1090,7 @@ def test_1d_slice_nonexistent_dimension_given(self):
             _ = self.cube.slices_over(self.cube.ndim + 1)
 
     def test_2d_slice_coord_given(self):
-        # Slicing over these two dimensions returns 420 2D cubes, so only check
+        # Slicing over these two dimensions returns 42 2D cubes, so only check
         # cubes up to `self.long_iterator_max` to keep test runtime sensible.
         res = self.cube.slices_over(
             [self.cube.coord("time"), self.cube.coord("model_level_number")]
@@ -1094,7 +1109,7 @@ def test_2d_slice_nonexistent_coord_given(self):
             )
 
     def test_2d_slice_coord_name_given(self):
-        # Slicing over these two dimensions returns 420 2D cubes, so only check
+        # Slicing over these two dimensions returns 42 2D cubes, so only check
         # cubes up to `self.long_iterator_max` to keep test runtime sensible.
         res = self.cube.slices_over(["time", "model_level_number"])
         for ct in range(self.long_iterator_max):
@@ -1109,7 +1124,7 @@ def test_2d_slice_nonexistent_coord_name_given(self):
             _ = self.cube.slices_over(["time", "wibble"])
 
     def test_2d_slice_dimension_given(self):
-        # Slicing over these two dimensions returns 420 2D cubes, so only check
+        # Slicing over these two dimensions returns 42 2D cubes, so only check
         # cubes up to `self.long_iterator_max` to keep test runtime sensible.
         res = self.cube.slices_over([0, 1])
         for ct in range(self.long_iterator_max):
@@ -1135,11 +1150,11 @@ def test_2d_slice_nonexistent_dimension_given(self):
             _ = self.cube.slices_over([0, self.cube.ndim + 1])
 
     def test_multidim_slice_coord_given(self):
-        # Slicing over surface altitude returns 100x100 2D cubes, so only check
+        # Slicing over surface altitude returns 10x10 2D cubes, so only check
         # cubes up to `self.long_iterator_max` to keep test runtime sensible.
         res = self.cube.slices_over("surface_altitude")
         # Define special ndindex iterator for the different dims sliced over.
-        nditer = np.ndindex(1, 1, 100, 100)
+        nditer = np.ndindex(1, 1, 10, 10)
         for ct in range(self.long_iterator_max):
             indices = list(next(nditer))
             # Replace the dimensions not iterated over with spanning slices.
diff --git a/noxfile.py b/noxfile.py
index 2eb46de349..d74a964e94 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -291,3 +291,27 @@ def wheel(session: nox.sessions.Session):
         "import iris; print(f'{iris.__version__=}')",
         external=True,
     )
+
+
+@nox.session
+def benchmarks(session: nox.sessions.Session):
+    """Run the Iris benchmark runner. Run session with `-- --help` for help.
+
+    Parameters
+    ----------
+    session : object
+        A `nox.sessions.Session` object.
+
+    """
+    if len(session.posargs) == 0:
+        message = (
+            "This session MUST be run with at least one argument. The "
+            "arguments are passed down to the benchmark runner script. E.g:\n"
+            "nox -s benchmarks -- --help\n"
+            "nox -s benchmarks -- something --help\n"
+            "nox -s benchmarks -- something\n"
+        )
+        session.error(message)
+    session.install("asv", "nox")
+    bm_runner_path = Path(__file__).parent / "benchmarks" / "bm_runner.py"
+    session.run("python", bm_runner_path, *session.posargs)
diff --git a/pyproject.toml b/pyproject.toml
index 82f7017f59..5840b77b85 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -133,7 +133,7 @@ local_scheme = "dirty-tag"
 version_scheme = "release-branch-semver"
 
 [tool.pytest.ini_options]
-addopts = "-ra"
+addopts = "-ra --durations=25"
 testpaths = "lib/iris"
 
 [tool.coverage.run]
@@ -153,8 +153,8 @@ exclude_lines = [
 ]
 
 [tool.codespell]
-ignore-words-list = "alpha-numeric,degreee,discontiguities,lazyness,meaned,nin"
-skip = "_build,*.css,*.ipynb,*.js,*.html,*.svg,*.xml,.git,generated"
+ignore-words-list = "alpha-numeric,assertIn,degreee,discontiguities,lazyness,meaned,nin"
+skip = "./CODE_OF_CONDUCT.md,_build,*.css,*.ipynb,*.js,*.html,*.svg,*.xml,.git,generated"
 
 [tool.check-manifest]
 ignore = [
diff --git a/requirements/locks/py310-linux-64.lock b/requirements/locks/py310-linux-64.lock
index ad0599da50..72af161ae1 100644
--- a/requirements/locks/py310-linux-64.lock
+++ b/requirements/locks/py310-linux-64.lock
@@ -1,26 +1,26 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: 7b9e397b762637f1bcebfdc117c9431d3c450c7626a34f05a5c57825d448507b
+# input_hash: a45f73f1b97a56ff99d022f070c005c4df54d79339b00f9e2eed332b90811baf
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81
 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.2.2-hbcca054_0.conda#2f4327a1cbe7f022401b236e915a5fef
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb
-https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_1.conda#6185f640c43843e5ad6fd1c5372c3f80
-https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-h41732ed_0.conda#7aca3059a1729aa76c597603f10b0dd3
-https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-h7e041cc_5.conda#f6f6600d18a4047b54f803cf708b868a
+https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda#cbbe59391138ea5ad3658c76912e147f
+https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-hf3520f5_1.conda#33b7851c39c25da14f6a233a8ccbeeca
+https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-hc0a3c3a_7.conda#53ebd4c833fa01cb2c6353e99f905406
 https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.11.3-h59595ed_0.conda#df9ae69b85e0cab9bde23eff1e87f183
 https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.10-4_cp310.conda#26322ec5d7712c3ded99dd656142b8ce
 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8
 https://conda.anaconda.org/conda-forge/linux-64/utfcpp-4.0.5-ha770c72_0.conda#25965c1d1d5fc00ce2b663b73008e3b7
 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29
-https://conda.anaconda.org/conda-forge/linux-64/libgomp-13.2.0-h807b86a_5.conda#d211c42b9ce49aee3734fdc828731689
+https://conda.anaconda.org/conda-forge/linux-64/libgomp-13.2.0-h77fa898_7.conda#abf3fec87c2563697defa759dec3d639
 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2#73aaf86a425cc6e73fcf236a5a46396d
 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab
-https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h807b86a_5.conda#d4ff227c46917d3b4565302a2bbb276b
+https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h77fa898_7.conda#72ec1b1b04c4d15d4204ece1ecea5978
 https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.11-hd590300_1.conda#0bb492cca54017ea314b809b1ee3a176
-https://conda.anaconda.org/conda-forge/linux-64/aom-3.8.2-h59595ed_0.conda#625e1fed28a5139aed71b3a76117ef84
+https://conda.anaconda.org/conda-forge/linux-64/aom-3.9.0-hac33072_0.conda#93a3bf248e5bc729807db198a9c89f07
 https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2#d9c69a24ad678ffce24c6543a0176b00
 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hd590300_5.conda#69b8b6202a07720f448be700e300ccf4
 https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.28.1-hd590300_0.conda#dcde58ff9a1f30b0037a2315d1846d1f
@@ -29,8 +29,8 @@ https://conda.anaconda.org/conda-forge/linux-64/double-conversion-3.3.0-h59595ed
 https://conda.anaconda.org/conda-forge/linux-64/eigen-3.4.0-h00ab1b0_0.conda#b1b879d6d093f55dd40d58b5eb2f0699
 https://conda.anaconda.org/conda-forge/linux-64/fribidi-1.0.10-h36c2ea0_0.tar.bz2#ac7bc6a654f8f41b352b38f4051135f8
 https://conda.anaconda.org/conda-forge/linux-64/geos-3.12.1-h59595ed_0.conda#8c0f4f71f5a59ceb0c6fa9f51501066d
-https://conda.anaconda.org/conda-forge/linux-64/gettext-0.21.1-h27087fc_0.tar.bz2#14947d8770185e5153fdd04d4673ed37
-https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.1-h0b41bf4_3.conda#96f3b11872ef6fad973eac856cd2624f
+https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.22.5-h59595ed_2.conda#985f2f453fb72408d6b6f1be0f324033
+https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.2-hd590300_0.conda#3bf7b9fd5a7136126e0234db4b87c8b6
 https://conda.anaconda.org/conda-forge/linux-64/gmp-6.3.0-h59595ed_1.conda#e358c7c5f6824c272b5034b3816438a7
 https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-h59595ed_1003.conda#f87c7b7c2cb45f323ffbce941c78ab7c
 https://conda.anaconda.org/conda-forge/linux-64/icu-73.2-h59595ed_0.conda#cc47e1facc155f91abd89b11e48e72ff
@@ -38,14 +38,16 @@ https://conda.anaconda.org/conda-forge/linux-64/jsoncpp-1.9.5-h4bd325d_1.tar.bz2
 https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3
 https://conda.anaconda.org/conda-forge/linux-64/lame-3.100-h166bdaf_1003.tar.bz2#a8832b479f93521a9e7b5b743803be51
 https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2#76bbff344f0134279f225174e9064c8f
-https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240116.1-cxx17_h59595ed_2.conda#75648bc5dd3b8eab22406876c24d81ec
+https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240116.2-cxx17_h59595ed_0.conda#682bdbe046a68f749769b492f3625c5c
 https://conda.anaconda.org/conda-forge/linux-64/libaec-1.1.3-h59595ed_0.conda#5e97e271911b8b2001a8b71860c32faa
+https://conda.anaconda.org/conda-forge/linux-64/libasprintf-0.22.5-h661eb56_2.conda#dd197c968bf9760bba0031888d431ede
 https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hd590300_1.conda#aec6c91c7371c26392a06708a73c70e5
 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.20-hd590300_0.conda#8e88f9389f1165d7c0936fe40d9a9a79
 https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda#172bf1cd1ff8629f2b1179945ed45055
 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.5.0-hcb278e6_1.conda#6305a3dd2752c76335295da4e581f2fd
 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2#d645c6d2ac96843a2bfaccd2d62b3ac3
-https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-ha4646dd_5.conda#7a6bd7a12a4bd359e2afe6c0fa1acace
+https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-0.22.5-h59595ed_2.conda#172bcc51059416e7ce99e7b528cede83
+https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-hca663fb_7.conda#c0bd771f09a326fdcd95a60b617795bf
 https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda#d66573916ffcf376178462f1b61c941e
 https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda#ea25936bb4080d843790b586850f82b8
 https://conda.anaconda.org/conda-forge/linux-64/libmo_unpack-3.1.2-hf484d3e_1001.tar.bz2#95f32a6a5a666d33886ca5627239f03d
@@ -57,22 +59,22 @@ https://conda.anaconda.org/conda-forge/linux-64/libtasn1-4.19.0-h166bdaf_0.tar.b
 https://conda.anaconda.org/conda-forge/linux-64/libunistring-0.9.10-h7f98852_0.tar.bz2#7245a044b4a1980ed83196176b78b73a
 https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b
 https://conda.anaconda.org/conda-forge/linux-64/libvpx-1.14.0-h59595ed_0.conda#01c76c6d71097a0f3bd8683a8f255123
-https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.3.2-hd590300_0.conda#30de3fd9b3b602f7473f30e684eeea8c
+https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.4.0-hd590300_0.conda#b26e8aa824079e1be0294e7152ca4559
 https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc
-https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.13-hd590300_5.conda#f36c115f1ee199da648e0597ec2047ad
+https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.13-h4ab18f5_6.conda#27329162c0dc732bcf67a4e0cd488125
 https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.4-hcb278e6_0.conda#318b08df404f9c9be5712aaa5a6f0bb0
 https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9160cdeb523a1b20cf8d2a0bf821f45d
-https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.4.20240210-h59595ed_0.conda#97da8860a0da5413c7c98a3b3838a645
+https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69
 https://conda.anaconda.org/conda-forge/linux-64/nettle-3.9.1-h7ab15ed_0.conda#2bf1915cc107738811368afcb0993a59
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 https://conda.anaconda.org/conda-forge/linux-64/openh264-2.4.1-h59595ed_0.conda#3dfcf61b8e78af08110f5229f79580af
-https://conda.anaconda.org/conda-forge/linux-64/openssl-3.2.1-hd590300_1.conda#9d731343cff6ee2e5a25c4a091bf8e2a
+https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_3.conda#12ea6d0d4ed54530eaed18e4835c1f7c
 https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#71004cbf7924e19c02746ccde9fd7123
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 https://conda.anaconda.org/conda-forge/linux-64/pugixml-1.14-h59595ed_0.conda#2c97dd90633508b422c11bd3018206ab
-https://conda.anaconda.org/conda-forge/linux-64/snappy-1.1.10-h9fff704_0.conda#e6d228cd0bb74a51dd18f5bfce0b4115
-https://conda.anaconda.org/conda-forge/linux-64/svt-av1-2.0.0-h59595ed_0.conda#207e01ffa0eb2d2efb83fb6f46365a21
+https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.0-hdb0a2a9_1.conda#843bbb8ace1d64ac50d64639ff38b014
+https://conda.anaconda.org/conda-forge/linux-64/svt-av1-2.1.0-hac33072_0.conda#2a08edb7cd75e56623f2712292a97325
 https://conda.anaconda.org/conda-forge/linux-64/x264-1!164.3095-h166bdaf_2.tar.bz2#6c99772d483f566d59e25037fea2c4b1
 https://conda.anaconda.org/conda-forge/linux-64/x265-3.5-h924138e_3.tar.bz2#e7f6ed84d4623d52ee581325c1587a6b
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@@ -88,25 +90,24 @@ https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.6-h166bdaf_0.tar.bz2#2161
 https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h7f98852_2.tar.bz2#4cb3ad778ec2d5a7acbdf254eb1c42ae
 https://conda.anaconda.org/conda-forge/linux-64/expat-2.5.0-hcb278e6_1.conda#8b9b5aca60558d02ddaa09d599e55920
 https://conda.anaconda.org/conda-forge/linux-64/hdf4-4.2.15-h2a13503_7.conda#bd77f8da987968ec3927990495dc22e4
+https://conda.anaconda.org/conda-forge/linux-64/libasprintf-devel-0.22.5-h661eb56_2.conda#02e41ab5834dcdcc8590cf29d9526f50
 https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hd590300_1.conda#f07002e225d7a60a694d42a7bf5ff53f
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 https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda#a1cfcc585f0c42bf8d5546bb1dfb668d
-https://conda.anaconda.org/conda-forge/linux-64/libflac-1.4.3-h59595ed_0.conda#ee48bf17cc83a00f59ca1494d5646869
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diff --git a/requirements/locks/py311-linux-64.lock b/requirements/locks/py311-linux-64.lock
index 0823ffba42..1e6410889b 100644
--- a/requirements/locks/py311-linux-64.lock
+++ b/requirements/locks/py311-linux-64.lock
@@ -1,36 +1,36 @@
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+# input_hash: 921f01115b09c230eeef12b4d97805af9e292b23bd868d0f684d1b83da2830e9
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diff --git a/requirements/locks/py312-linux-64.lock b/requirements/locks/py312-linux-64.lock
index f20fd59dbc..9e1f9abc1d 100644
--- a/requirements/locks/py312-linux-64.lock
+++ b/requirements/locks/py312-linux-64.lock
@@ -1,26 +1,26 @@
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+# input_hash: ffd09acb543fe189cd316bb3e8a3b423b7091e6236d2ba943525acbd339930d3
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-https://conda.anaconda.org/conda-forge/noarch/pyvista-0.43.4-pyhd8ed1ab_0.conda#21e567168518369ce3f1c51e69b8ac0e
+https://conda.anaconda.org/conda-forge/noarch/pyvista-0.43.8-pyhd8ed1ab_0.conda#e0c5b99bd2a16284f55322d0fb28cb39
 https://conda.anaconda.org/conda-forge/noarch/geovista-0.4.1-pyhd8ed1ab_0.conda#8dbe5526321fa7f1cb4dbc4f1644dcb3
-https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.15.2-pyhd8ed1ab_0.conda#ce99859070b0e17ccc63234ca58f3ed8
+https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.15.3-pyhd8ed1ab_0.conda#55e445f4fcb07f2471fb0e1102d36488
 https://conda.anaconda.org/conda-forge/noarch/sphinx-copybutton-0.5.2-pyhd8ed1ab_0.conda#ac832cc43adc79118cf6e23f1f9b8995
 https://conda.anaconda.org/conda-forge/noarch/sphinx-design-0.5.0-pyhd8ed1ab_0.conda#264b3c697fa9cdade87eb0abe4440d54
-https://conda.anaconda.org/conda-forge/noarch/sphinx-gallery-0.15.0-pyhd8ed1ab_0.conda#1a49ca9515ef9a96edff2eea06143dc6
+https://conda.anaconda.org/conda-forge/noarch/sphinx-gallery-0.16.0-pyhd8ed1ab_0.conda#add28691ee89e875b190eda07929d5d4
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-1.0.8-pyhd8ed1ab_0.conda#611a35a27914fac3aa37611a6fe40bb5
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-1.0.6-pyhd8ed1ab_0.conda#d7e4954df0d3aea2eacc7835ad12671d
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.0.5-pyhd8ed1ab_0.conda#7e1e7437273682ada2ed5e9e9714b140
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-1.0.7-pyhd8ed1ab_0.conda#26acae54b06f178681bfb551760f5dd1
-https://conda.anaconda.org/conda-forge/noarch/sphinx-7.2.6-pyhd8ed1ab_0.conda#bbfd1120d1824d2d073bc65935f0e4c0
+https://conda.anaconda.org/conda-forge/noarch/sphinx-7.3.7-pyhd8ed1ab_0.conda#7b1465205e28d75d2c0e1a868ee00a67
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_0.conda#e507335cb4ca9cff4c3d0fa9cdab255e
diff --git a/tools/release_do_nothing.py b/tools/release_do_nothing.py
index 634e6a65a2..79f26f3409 100755
--- a/tools/release_do_nothing.py
+++ b/tools/release_do_nothing.py
@@ -327,7 +327,7 @@ def finalise_whats_new(
 
     message = (
         "Commit and push all the What's New changes.\n"
-        f'git commit -am "Whats new updates for {release_strings.tag} .";\n'
+        f"git commit -am \"What's new updates for {release_strings.tag} .\";\n"
         f"git push -u origin {working_branch};"
     )
     _wait_for_done(message)
@@ -759,7 +759,7 @@ def merge_back(
 
         message = (
             "Commit and push all the What's New changes.\n"
-            'git commit -am "Restore latest Whats New files.";\n'
+            "git commit -am \"Restore latest What's New files.\";\n"
             f"git push -u origin {working_branch};"
         )
         _wait_for_done(message)