diff --git a/CHANGES.rst b/CHANGES.rst
index 81fe0855c..89e8b30f1 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -6,6 +6,17 @@ Changelog
 -------------------
 Contributors to this version: Trevor James Smith (:user:`Zeitsperre`).
 
+New features and enhancements
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+* Two previously private functions for selecting a day of year in a time series when performing calendar conversions are now exposed. (:issue:`1305`, :pull:`1317`). New functions are:
+    * ``xclim.core.calendar.yearly_interpolated_doy``
+    * ``xclim.core.calendar.yearly_random_doy``
+
+Breaking changes
+^^^^^^^^^^^^^^^^
+* The call signatures for ``xclim.ensembles.create_ensemble`` and ``xclim.ensembles._base._ens_align_dataset`` have been deprecated. Calls to these functions made with the original signature will emit warnings. Changes will become breaking in `xclim>=0.43.0`.(:issue:`1305`, :pull:`1317`). Affected variable:
+    * `mf_flag` (bool) -> `multifile` (bool)
+
 Internal changes
 ^^^^^^^^^^^^^^^^
 * Added `xclim` to the `ouranos Zenodo community <https://zenodo.org/communities/ouranos/>`_ . (:pull:`1313`).
@@ -21,6 +32,7 @@ Internal changes
     * Markdown explanations in some Jupyter Notebooks have been edited for clarity
 * Removed `Mapping` abstract base class types in call signatures (`dict` variables were always expected). (:pull:`1308`).
 * Changes in testing setup now prevent ``test_mean_radiant_temperature`` from sometimes causing a segmentation fault. (:issue:`1303`, :pull:`1315`).
+* Addressed a formatting bug that caused `Indicators` with multiple variables returned to not be properly formatted in the documentation. (:issue:`1305`, :pull:`1317`).
 
 0.41.0 (2023-02-28)
 -------------------
diff --git a/docs/api.rst b/docs/api.rst
index 83d731b70..55b30dc2e 100644
--- a/docs/api.rst
+++ b/docs/api.rst
@@ -83,22 +83,22 @@ Ensembles Module
 
 .. Use of autofunction is so that paths do not include private modules.
 .. autofunction:: xclim.ensembles.kkz_reduce_ensemble
-    :noindex:
+   :noindex:
 
 .. autofunction:: xclim.ensembles.kmeans_reduce_ensemble
-    :noindex:
+   :noindex:
 
 .. autofunction:: xclim.ensembles.plot_rsqprofile
-    :noindex:
+   :noindex:
 
 .. automodule:: xclim.ensembles._robustness
-    :noindex:
+   :noindex:
 
 .. autofunction:: xclim.ensembles.change_significance
-    :noindex:
+   :noindex:
 
 .. autofunction:: xclim.ensembles.robustness_coefficient
-    :noindex:
+   :noindex:
 
 Units Handling Submodule
 ========================
diff --git a/docs/indices.rst b/docs/indices.rst
index e19a06969..5ec6fc0c7 100644
--- a/docs/indices.rst
+++ b/docs/indices.rst
@@ -4,10 +4,32 @@ Climate Indices
 
 .. note::
 
-    Climate `Indices` serve as the driving mechanisms behind `Indicators` and should be used in cases where default settings for an Indicator may need to be tweaked, metadata completeness is not required, or a user wishes to design a virtual module from existing indices (e.g. see :ref:`notebooks/extendxclim:Defining new indicators`).
+    Climate `Indices` serve as the driving mechanisms behind `Indicators` and should be used in cases where
+    default settings for an Indicator may need to be tweaked, metadata completeness is not required, or a user
+    wishes to design a virtual module from existing indices (see: :ref:`notebooks/extendxclim:Defining new indicators`).
 
     For higher-level and general purpose use, the xclim developers suggest using the :ref:`indicators:Climate Indicators`.
 
+Indices Library
+---------------
+
+Climate indices functions are designed to operate on :py:class:`xarray.DataArray` objects.
+Most of these functions operate on daily time series, but in some cases might accept other sampling
+frequencies as well. All functions perform units checks to make sure that inputs have the expected dimensions
+(e.g. handling for units of temperature, whether they are Celsius, kelvin or Fahrenheit), and set the `units`
+attribute of the output `DataArray`.
+
+The :py:mod:`xclim.indices.generic`, :py:mod:`xclim.indices.helpers`, :py:mod:`xclim.indices.run_length`, and
+:py:mod:`xclim.indices.stats` submodules provide helper functions to simplify the implementation of indices
+while functions under :py:mod:`xclim.core.calendar` can aid with challenges arising from variable calendar
+types.
+
+.. warning::
+
+    Indices functions do not perform missing value checks, and usually do not set CF-Convention attributes
+    (long_name, standard_name, description, cell_methods, etc.). These functionalities are provided by
+    :py:class:`xclim.core.indicator.Indicator` instances found in the :py:mod:`xclim.indicators.atmos`,
+    :py:mod:`xclim.indicators.land` and :mod:`xclim.indicators.seaIce` modules.
 
 .. automodule:: xclim.indices
    :members:
diff --git a/xclim/core/calendar.py b/xclim/core/calendar.py
index a853d687e..fe56397db 100644
--- a/xclim/core/calendar.py
+++ b/xclim/core/calendar.py
@@ -34,7 +34,9 @@
 __all__ = [
     "DayOfYearStr",
     "adjust_doy_calendar",
+    "build_climatology_bounds",
     "climatological_mean_doy",
+    "common_calendar",
     "compare_offsets",
     "convert_calendar",
     "date_range",
@@ -52,9 +54,10 @@
     "resample_doy",
     "select_time",
     "time_bnds",
-    "within_bnds_doy",
     "uniform_calendars",
-    "build_climatology_bounds",
+    "within_bnds_doy",
+    "yearly_interpolated_doy",
+    "yearly_random_doy",
 ]
 
 # Maximum day of year in each calendar.
@@ -96,6 +99,43 @@ def date_range(
     return xr.cftime_range(*args, calendar=calendar, **kwargs)
 
 
+def yearly_interpolated_doy(
+    time: pd.DatetimeIndex | CFTimeIndex, source_calendar: str, target_calendar: str
+):
+    """Return the nearest day in the target calendar of the corresponding "decimal year" in the source calendar."""
+    yr = int(time.dt.year[0])
+    return np.round(
+        days_in_year(yr, target_calendar)
+        * time.dt.dayofyear
+        / days_in_year(yr, source_calendar)
+    ).astype(int)
+
+
+def yearly_random_doy(
+    time: pd.DatetimeIndex | CFTimeIndex,
+    rng: np.random.Generator,
+    source_calendar: str,
+    target_calendar: str,
+):
+    """Return a day of year in the new calendar.
+
+    Removes Feb 29th and five other days chosen randomly within five sections of 72 days.
+    """
+    yr = int(time.dt.year[0])
+    new_doy = np.arange(360) + 1
+    rm_idx = rng.integers(0, 72, 5) + (np.arange(5) * 72)
+    if source_calendar == "360_day":
+        for idx in rm_idx:
+            new_doy[idx + 1 :] = new_doy[idx + 1 :] + 1
+        if days_in_year(yr, target_calendar) == 366:
+            new_doy[new_doy >= 60] = new_doy[new_doy >= 60] + 1
+    elif target_calendar == "360_day":
+        new_doy = np.insert(new_doy, rm_idx - np.arange(5), -1)
+        if days_in_year(yr, source_calendar) == 366:
+            new_doy = np.insert(new_doy, 60, -1)
+    return new_doy[time.dt.dayofyear - 1]
+
+
 def get_calendar(obj: Any, dim: str = "time") -> str:
     """Return the calendar of an object.
 
@@ -321,38 +361,18 @@ def convert_calendar(
     # TODO Maybe the 5-6 days to remove could be given by the user?
     if align_on in ["year", "random"]:
         if align_on == "year":
+            new_doy = source.time.groupby(f"{dim}.year").map(
+                yearly_interpolated_doy,
+                source_calendar=cal_src,
+                target_calendar=cal_tgt,
+            )
 
-            def _yearly_interp_doy(time):
-                # Returns the nearest day in the target calendar of the corresponding "decimal year" in the source calendar
-                yr = int(time.dt.year[0])
-                return np.round(
-                    days_in_year(yr, cal_tgt)
-                    * time.dt.dayofyear
-                    / days_in_year(yr, cal_src)
-                ).astype(int)
-
-            new_doy = source.time.groupby(f"{dim}.year").map(_yearly_interp_doy)
         elif align_on == "random":
-
-            def _yearly_random_doy(time, rng):
-                # Return a doy in the new calendar, removing the Feb 29th and 5 other
-                # days chosen randomly within 5 sections of 72 days.
-                yr = int(time.dt.year[0])
-                new_doy = np.arange(360) + 1
-                rm_idx = rng.integers(0, 72, 5) + (np.arange(5) * 72)
-                if cal_src == "360_day":
-                    for idx in rm_idx:
-                        new_doy[idx + 1 :] = new_doy[idx + 1 :] + 1
-                    if days_in_year(yr, cal_tgt) == 366:
-                        new_doy[new_doy >= 60] = new_doy[new_doy >= 60] + 1
-                elif cal_tgt == "360_day":
-                    new_doy = np.insert(new_doy, rm_idx - np.arange(5), -1)
-                    if days_in_year(yr, cal_src) == 366:
-                        new_doy = np.insert(new_doy, 60, -1)
-                return new_doy[time.dt.dayofyear - 1]
-
             new_doy = source.time.groupby(f"{dim}.year").map(
-                _yearly_random_doy, rng=np.random.default_rng()
+                yearly_random_doy,
+                rng=np.random.default_rng(),
+                source_calendar=cal_src,
+                target_calendar=cal_tgt,
             )
 
         # Convert the source datetimes, but override the doy with our new doys
diff --git a/xclim/core/formatting.py b/xclim/core/formatting.py
index 08a642925..63fa509f3 100644
--- a/xclim/core/formatting.py
+++ b/xclim/core/formatting.py
@@ -600,7 +600,7 @@ def _gen_returns_section(cf_attrs: Sequence[dict[str, Any]]):
                     attr = "<Dynamically generated string>"
                 added_section += f" **{key}**: {attr};"
         if added_section:
-            section = f"{section}, with additional attributes:{added_section[:-1]}"
+            section = f"{section}, with additional attributes:{added_section[:-1]}\n"
     return section
 
 
diff --git a/xclim/core/units.py b/xclim/core/units.py
index b91444bba..44b3f5b5e 100644
--- a/xclim/core/units.py
+++ b/xclim/core/units.py
@@ -932,7 +932,7 @@ def check_units(val: str | int | float | None, dim: str | None) -> None:
 
 
 def declare_units(
-    **units_by_name: dict[str, str],
+    **units_by_name: str,
 ) -> Callable:
     """Create a decorator to check units of function arguments.
 
diff --git a/xclim/ensembles/_base.py b/xclim/ensembles/_base.py
index c9ecf1af3..08874e441 100644
--- a/xclim/ensembles/_base.py
+++ b/xclim/ensembles/_base.py
@@ -5,6 +5,7 @@
 """
 from __future__ import annotations
 
+import warnings
 from glob import glob
 from pathlib import Path
 from typing import Any, Sequence
@@ -19,11 +20,12 @@
 
 def create_ensemble(
     datasets: Any,
-    mf_flag: bool = False,
+    multifile: bool = False,
     resample_freq: str | None = None,
     calendar: str | None = None,
     realizations: Sequence[Any] | None = None,
     cal_kwargs: dict | None = None,
+    mf_flag: bool | str = "UNSET",  # noqa
     **xr_kwargs,
 ) -> xr.Dataset:
     """Create an xarray dataset of an ensemble of climate simulation from a list of netcdf files.
@@ -38,15 +40,15 @@ def create_ensemble(
     Parameters
     ----------
     datasets : list or dict or string
-      List of netcdf file paths or xarray Dataset/DataArray objects . If mf_flag is True, ncfiles should be a list of
-      lists where each sublist contains input .nc files of an xarray multifile Dataset.
+      List of netcdf file paths or xarray Dataset/DataArray objects . If `multifile` is True, ncfiles should be a
+      list of lists where each sublist contains input .nc files of an xarray multifile Dataset.
       If DataArray objects are passed, they should have a name in order to be transformed into Datasets.
       A dictionary can be passed instead of a list, in which case the keys are used as coordinates along the new
       `realization` axis.
       If a string is passed, it is assumed to be a glob pattern for finding datasets.
-    mf_flag : bool
+    multifile : bool
       If True, climate simulations are treated as xarray multifile Datasets before concatenation.
-      Only applicable when "datasets" is sequence of list of file paths.
+      Only applicable when "datasets" is sequence of list of file paths. Default: False.
     resample_freq : Optional[str]
       If the members of the ensemble have the same frequency but not the same offset, they cannot be properly aligned.
       If resample_freq is set, the time coordinate of each member will be modified to fit this frequency.
@@ -54,16 +56,16 @@ def create_ensemble(
       The calendar of the time coordinate of the ensemble.
       By default, the smallest common calendar is chosen. For example, a mixed input of "noleap" and "360_day" will default to "noleap".
       'default' is the standard calendar using np.datetime64 objects (xarray's "standard" with `use_cftime=False`).
-    realizations: sequence, optional
+    realizations : sequence, optional
       The coordinate values for the new `realization` axis.
       If None (default), the new axis has a simple integer coordinate.
       This argument shouldn't be used if `datasets` is a glob pattern as the dataset order is random.
     cal_kwargs : dict, optional
-      Additionnal arguments to pass to py:func:`xclim.core.calendar.convert_calendar`.
+      Additional arguments to pass to py:func:`xclim.core.calendar.convert_calendar`.
       For conversions involving '360_day', the align_on='date' option is used by default.
     **xr_kwargs
       Any keyword arguments to be given to `xr.open_dataset` when opening the files
-      (or to `xr.open_mfdataset` if mf_flag is True)
+      (or to `xr.open_mfdataset` if `multifile` is True)
 
     Returns
     -------
@@ -90,7 +92,7 @@ def create_ensemble(
 
         # Simulation 2 is also a list of .nc files:
         datasets.extend(Path("/dir2").glob("*.nc"))
-        ens = create_ensemble(datasets, mf_flag=True)
+        ens = create_ensemble(datasets, multifile=True)
     """
     if isinstance(datasets, dict):
         if realizations is None:
@@ -103,9 +105,18 @@ def create_ensemble(
             "is a glob pattern, as the final order is random."
         )
 
+    if mf_flag != "UNSET":
+        warnings.warn(
+            "The `mf_flag` argument is being deprecated in favour of `multifile` in `create.ensemble()`. "
+            "This change will be made effective from `xclim>=0.43.0`. Please update your scripts accordingly",
+            FutureWarning,
+            stacklevel=3,
+        )
+        multifile = mf_flag
+
     ds = _ens_align_datasets(
         datasets,
-        mf_flag,
+        multifile,
         resample_freq,
         calendar=calendar,
         cal_kwargs=cal_kwargs or {},
@@ -118,8 +129,8 @@ def create_ensemble(
     dim = xr.IndexVariable("realization", list(realizations), attrs={"axis": "E"})
 
     ens = xr.concat(ds, dim)
-    for vname, var in ds[0].variables.items():
-        ens[vname].attrs.update(**var.attrs)
+    for var_name, var in ds[0].variables.items():
+        ens[var_name].attrs.update(**var.attrs)
     ens.attrs.update(**ds[0].attrs)
 
     return ens
@@ -336,10 +347,11 @@ def ensemble_percentiles(
 
 def _ens_align_datasets(
     datasets: list[xr.Dataset | Path | str | list[Path | str]] | str,
-    mf_flag: bool = False,
+    multifile: bool = False,
     resample_freq: str | None = None,
     calendar: str = "default",
     cal_kwargs: dict | None = None,
+    mf_flag: bool | str = "UNSET",  # noqa
     **xr_kwargs,
 ) -> list[xr.Dataset]:
     """Create a list of aligned xarray Datasets for ensemble Dataset creation.
@@ -347,11 +359,11 @@ def _ens_align_datasets(
     Parameters
     ----------
     datasets : list[xr.Dataset | xr.DataArray | Path | str | list[Path | str]] or str
-        List of netcdf file paths or xarray Dataset/DataArray objects . If mf_flag is True, 'datasets' should be a list
-        of lists where each sublist contains input NetCDF files of a xarray multi-file Dataset.
+        List of netcdf file paths or xarray Dataset/DataArray objects . If `multifile` is True, 'datasets' should be a
+        list of lists where each sublist contains input NetCDF files of a xarray multi-file Dataset.
         DataArrays should have a name, so they can be converted to datasets.
         If a string, it is assumed to be a glob pattern for finding datasets.
-    mf_flag : bool
+    multifile : bool
         If True climate simulations are treated as xarray multi-file datasets before concatenation.
         Only applicable when 'datasets' is a sequence of file paths.
     resample_freq : str, optional
@@ -375,10 +387,19 @@ def _ens_align_datasets(
     if isinstance(datasets, str):
         datasets = glob(datasets)
 
+    if mf_flag != "UNSET":
+        warnings.warn(
+            "The `mf_flag` argument is being deprecated in favour of `multifile` in `_ens_align_datasets()`. "
+            "This change will be made effective from `xclim>=0.43.0`. Please update your scripts accordingly",
+            FutureWarning,
+            stacklevel=3,
+        )
+        multifile = mf_flag
+
     ds_all = []
     calendars = []
     for i, n in enumerate(datasets):
-        if mf_flag:
+        if multifile:
             ds = xr.open_mfdataset(n, combine="by_coords", **xr_kwargs)
         else:
             if isinstance(n, xr.Dataset):
diff --git a/xclim/ensembles/_robustness.py b/xclim/ensembles/_robustness.py
index aa6891dd9..799a6de10 100644
--- a/xclim/ensembles/_robustness.py
+++ b/xclim/ensembles/_robustness.py
@@ -23,7 +23,7 @@
 def change_significance(
     fut: xr.DataArray | xr.Dataset,
     ref: xr.DataArray | xr.Dataset = None,
-    test: str = "ttest",
+    test: str | None = "ttest",
     weights: xr.DataArray = None,
     p_vals: bool = False,
     **kwargs,
diff --git a/xclim/indices/__init__.py b/xclim/indices/__init__.py
index c170b9f96..7d587f2f8 100644
--- a/xclim/indices/__init__.py
+++ b/xclim/indices/__init__.py
@@ -1,27 +1,4 @@
-# noqa: D205,D400
-"""
-===============
-Indices Library
-===============
-
-This module contains climate indices functions operating on `xarray.DataArray`. Most of these
-functions operate on daily time series, but might accept other sampling frequencies as well. All
-functions perform units checks to make sure that inputs have the expected dimensions (for example
-have units of temperature, whether it is Celsius, kelvin or Fahrenheit), and set the `units`
-attribute of the output DataArray.
-
-The `calendar`, `fire`, `generic`, `helpers`, `run_length` and `stats` submodules provide
-helpers to simplify the implementation of the indices.
-
-.. note::
-
-    Indices functions do not perform missing value checks, and usually do not set CF-Convention attributes
-    (long_name, standard_name, description, cell_methods, etc.). These functionalities are provided by
-    :py:class:`xclim.indicators.Indicator` instances found in the :py:mod:`xclim.indicators.atmos`,
-    :py:mod:`xclim.indicators.land` and :mod:`xclim.indicators.seaIce` modules,
-    documented in :ref:`indicators:Climate Indicators`.
-
-"""
+"""Indices module."""
 from __future__ import annotations
 
 from ._agro import *
@@ -42,8 +19,8 @@
 )
 
 """
-Notes for docstring
--------------------
+Notes for docstrings
+--------------------
 
 The docstrings adhere to the `NumPy`_ style convention and is meant as a way to store CF-Convention metadata as
 well as information relevant to third party libraries (such as a WPS server).
diff --git a/xclim/indices/fire/_cffwis.py b/xclim/indices/fire/_cffwis.py
index 595acc6ab..326e88be7 100644
--- a/xclim/indices/fire/_cffwis.py
+++ b/xclim/indices/fire/_cffwis.py
@@ -256,7 +256,7 @@ def _fine_fuel_moisture_code(t, p, w, h, ffmc0):  # pragma: no cover
 
     Parameters
     ----------
-    t: array_like
+    t : array_like
         Noon temperature [C].
     p : array_like
         Rain fall in open over previous 24 hours, at noon [mm].
@@ -342,7 +342,7 @@ def _duff_moisture_code(
 
     Parameters
     ----------
-    t: array_like
+    t : array_like
         Noon temperature [C].
     p : array_like
         Rain fall in open over previous 24 hours, at noon [mm].
@@ -404,7 +404,7 @@ def _drought_code(
 
     Parameters
     ----------
-    t: array-like
+    t : array-like
         Noon temperature [C].
     p : array_like
         Rain fall in open over previous 24 hours, at noon [mm].
@@ -945,9 +945,9 @@ def fire_weather_ufunc(
         How to compute the start-up and shutdown of the fire season.
         If "None", no start-ups or shutdowns are computed, similar to the R fire function.
         Ignored if `season_mask` is given.
-    overwintering: bool
+    overwintering : bool
         Whether to activate DC overwintering or not. If True, either season_method or season_mask must be given.
-    dry_start: {None, 'CFS', 'GFWED'}
+    dry_start : {None, 'CFS', 'GFWED'}
         Whether to activate the DC and DMC "dry start" mechanism and which method to use. See Notes.
         If overwintering is activated, it overrides this parameter : only DMC is handled through the dry start mechanism.
     initial_start_up : bool
@@ -1316,9 +1316,9 @@ def cffwis_indices(
         How to compute the start-up and shutdown of the fire season.
         If "None", no start-ups or shutdowns are computed, similar to the R fire function.
         Ignored if `season_mask` is given.
-    overwintering: bool
+    overwintering : bool
         Whether to activate DC overwintering or not. If True, either season_method or season_mask must be given.
-    dry_start: {None, 'CFS', 'GFWED'}
+    dry_start : {None, 'CFS', 'GFWED'}
         Whether to activate the DC and DMC "dry start" mechanism or not, see :py:func:`fire_weather_ufunc`.
     initial_start_up : bool
         If True (default), gridpoints where the fire season is active on the first timestep go through a start_up phase
@@ -1420,9 +1420,9 @@ def drought_code(
         How to compute the start-up and shutdown of the fire season.
         If "None", no start-ups or shutdowns are computed, similar to the R fire function.
         Ignored if `season_mask` is given.
-    overwintering: bool
+    overwintering : bool
         Whether to activate DC overwintering or not. If True, either season_method or season_mask must be given.
-    dry_start: {None, "CFS", 'GFWED'}
+    dry_start : {None, "CFS", 'GFWED'}
         Whether to activate the DC and DMC "dry start" mechanism and which method to use.
         See :py:func:`fire_weather_ufunc`.
     initial_start_up : bool
@@ -1503,16 +1503,16 @@ def fire_season(
     freq : str, optional
         If given only the longest fire season for each period defined by this frequency,
         Every "seasons" are returned if None, including the short shoulder seasons.
-    temp_start_thresh: Quantified
+    temp_start_thresh : Quantified
         Minimal temperature needed to start the season. Must be scalar.
     temp_end_thresh : Quantified
         Maximal temperature needed to end the season. Must be scalar.
-    temp_condition_days: int
+    temp_condition_days : int
         Number of days with temperature above or below the thresholds to trigger a start or an end of the fire season.
-    snow_condition_days: int
+    snow_condition_days : int
         Parameters for the fire season determination. See :py:func:`fire_season`. Temperature is in degC, snow in m.
         The `snow_thresh` parameters is also used when `dry_start` is set to "GFWED".
-    snow_thresh: Quantified
+    snow_thresh : Quantified
         Minimal snow depth level to end a fire season, only used with method "LA08".
         Must be scalar.
 
diff --git a/xclim/testing/tests/test_ensembles.py b/xclim/testing/tests/test_ensembles.py
index 59b41da1c..068c3d8be 100644
--- a/xclim/testing/tests/test_ensembles.py
+++ b/xclim/testing/tests/test_ensembles.py
@@ -39,7 +39,6 @@ def test_create_ensemble(
             ds = open_dataset(n, decode_times=False)
             ds["time"] = xr.decode_cf(ds).time
             ds_all.append(ds)
-
         ens = ensembles.create_ensemble(ds_all)
 
         assert len(ens.realization) == len(ensemble_dataset_objects["nc_files_simple"])
@@ -135,8 +134,8 @@ def test_create_unaligned_times(self, timegen, calkw):
             ens = ensembles.create_ensemble((d1, d2))
             assert ens.time.size == 48
             np.testing.assert_equal(ens.isel(time=0), [np.nan, 0])
-
         ens = ensembles.create_ensemble((d1, d2), resample_freq="MS")
+
         assert ens.time.size == 24
         np.testing.assert_equal(ens.isel(time=0), [0, 0])
 
@@ -244,7 +243,8 @@ def test_calc_mean_std_min_max(self, ensemble_dataset_objects, open_dataset):
         for n in ensemble_dataset_objects["nc_files_simple"]:
             ds = open_dataset(n)
             ds_all.append(ds)
-        ens = ensembles.create_ensemble(ds_all)
+        with pytest.warns(FutureWarning):
+            ens = ensembles.create_ensemble(ds_all, mf_flag=False)
 
         out1 = ensembles.ensemble_mean_std_max_min(ens)
         np.testing.assert_array_equal(