diff --git a/docs/user_guide/convert_mom6.ipynb b/docs/user_guide/convert_mom6.ipynb
index d67f9a64c..738a45d92 100644
--- a/docs/user_guide/convert_mom6.ipynb
+++ b/docs/user_guide/convert_mom6.ipynb
@@ -149,6 +149,9 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.12.14"
+  },
+  "mystnb": {
+   "execution_in_temp": true
   }
  },
  "nbformat": 4,
diff --git a/docs/user_guide/fieldsets_grids.ipynb b/docs/user_guide/fieldsets_grids.ipynb
index a6fa91b17..ea19d640e 100644
--- a/docs/user_guide/fieldsets_grids.ipynb
+++ b/docs/user_guide/fieldsets_grids.ipynb
@@ -12,7 +12,7 @@
     "Parcels can natively handle two styles of grids; structured and unstructured, where Parcels `Field` objects exist on a (structured) {py:obj}`parcels.XGrid` and conform to [SGRID](https://sgrid.github.io/sgrid/) conventions, or on a (unstructured) {py:obj}`parcels.UxGrid` and conform to [UGRID](https://ugrid-conventions.github.io/ugrid-conventions/) conventions. Here we describe these grids on a conceptual level.\n",
     "\n",
     "```{note}\n",
-    "Parcels comes ready with a range of `convert` functions which will convert your `xarray` dataset into a SGRID or UGRID compliant dataset. See [this list](group_convert_models.md) for all the models we have `convert` functions for. If your model is not on that list, you can [create your own `convert` function](./convert_writing_functions.md), or reach out on our discussion board for help.\n",
+    "Parcels comes ready with a range of `convert` functions which will convert your `xarray` dataset into a SGRID or UGRID compliant dataset. See [this list](group_convert_ocean_models.md) for all the ocean models we have `convert` functions for, and [this list](group_convert_atmospheric_models.md) for all the atmosphere models we have `convert` functions for. If your model is not on that list, you can [create your own `convert` function](./convert_writing_functions.md), or reach out on our discussion board for help.\n",
     "```\n",
     "\n",
     "Under the hood, every `Field` in a `FieldSet` has a `grid` attribute. This `grid` stores the spatial and temporal information of the Field coordinates. The number of Grids in a FieldSet is thus always smaller or equal to the number of Field objects; and this is what the \"grid number\" column in {py:func}`parcels.FieldSet.describe()` refers to.\n",
diff --git a/src/parcels/_core/utils/time.py b/src/parcels/_core/utils/time.py
index fbe2056db..fdde89cf3 100644
--- a/src/parcels/_core/utils/time.py
+++ b/src/parcels/_core/utils/time.py
@@ -221,6 +221,8 @@ def float_to_datelike(dt: float, time_interval) -> np.datetime64 | np.timedelta6
         result = np.timedelta64(int(dt), "s") + time_interval.left
         if isinstance(result, cftime.datetime):
             return result
+        elif isinstance(result, np.timedelta64):
+            return result.astype("timedelta64[s]")
         return result.astype("datetime64[s]")
     else:
         return np.timedelta64(int(dt), "s")
diff --git a/tests/utils/test_time.py b/tests/utils/test_time.py
index 180bc3102..bb7ff2789 100644
--- a/tests/utils/test_time.py
+++ b/tests/utils/test_time.py
@@ -11,6 +11,7 @@
 from parcels._core.utils.time import (
     TimeInterval,
     _get_cf_attrs,
+    float_to_datelike,
     maybe_convert_python_timedelta_to_numpy,
     timedelta_to_float,
 )
@@ -175,6 +176,48 @@ def test_timedelta_to_float_exceptions():
         timedelta_to_float("invalid_type")
 
 
+@pytest.mark.parametrize(
+    "left,right,offset,expected",
+    [
+        (np.timedelta64(10500, "ms"), np.timedelta64(20, "s"), 2.9, np.timedelta64(12, "s")),
+        (np.timedelta64(3, "m"), np.timedelta64(5, "m"), -4.7, np.timedelta64(176, "s")),
+    ],
+)
+def test_float_to_datelike_timedelta_interval(left, right, offset, expected):
+    result = float_to_datelike(offset, TimeInterval(left, right))
+
+    assert isinstance(result, np.timedelta64)
+    assert result.dtype == np.dtype("timedelta64[s]")
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "interval,expected",
+    [
+        (None, np.timedelta64(2, "s")),
+        (
+            TimeInterval(np.datetime64("2023-01-01"), np.datetime64("2023-01-02")),
+            np.datetime64("2023-01-01T00:00:02", "s"),
+        ),
+        (
+            TimeInterval(
+                cftime_datetime(2023, 1, 1, calendar="360_day"), cftime_datetime(2023, 1, 2, calendar="360_day")
+            ),
+            cftime_datetime(2023, 1, 1, 0, 0, 2, calendar="360_day"),
+        ),
+    ],
+)
+def test_float_to_datelike_other_intervals(interval, expected):
+    result = float_to_datelike(2.9, interval)
+
+    assert isinstance(result, type(expected))
+    assert result == expected
+    if isinstance(expected, (np.datetime64, np.timedelta64)):
+        assert result.dtype == expected.dtype
+    elif isinstance(expected, cftime_datetime):
+        assert result.calendar == expected.calendar
+
+
 @given(pst.time.datetime_various())
 def test_datetime_get_cf_attrs(dt):
     attrs = _get_cf_attrs(dt)