Address additional upstream deprecation warnings

[spencerkclark]

Description

This PR addresses upstream errors surfaced in this latest build. Some representative failures can be found below:

Details

2026-04-11T12:05:56.8329679Z _____________ ERROR collecting xarray/tests/test_duck_array_ops.py _____________
2026-04-11T12:05:56.8330240Z xarray/tests/test_duck_array_ops.py:730: in <module>
2026-04-11T12:05:56.8331067Z     np.array([np.timedelta64(1, "h"), np.timedelta64("NaT")]),
2026-04-11T12:05:56.8331786Z                                       ^^^^^^^^^^^^^^^^^^^^^
2026-04-11T12:05:56.8332599Z E   DeprecationWarning: Using 'generic' unit for NumPy timedelta is deprecated, and will raise an error in the future. Please use a specific units instead.
2026-04-11T12:05:56.8333444Z ______________ ERROR at setup of test_field_access[360_day-year] _______________
2026-04-11T12:05:56.8334005Z [gw0] linux -- Python 3.13.13 /home/runner/work/xarray/xarray/.pixi/envs/test-nightly/bin/python
2026-04-11T12:05:56.8334362Z
2026-04-11T12:05:56.8334592Z times = array([cftime.Datetime360Day(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
2026-04-11T12:05:56.8335350Z        cftime.Datetime360Day(2000, 1, 1, 1, ... 0, has_year_zero=True),
2026-04-11T12:05:56.8335779Z        cftime.Datetime360Day(2000, 1, 5, 3, 0, 0, 0, has_year_zero=True)],
2026-04-11T12:05:56.8336112Z       dtype=object)
2026-04-11T12:05:56.8336230Z
2026-04-11T12:05:56.8336403Z     @pytest.fixture
2026-04-11T12:05:56.8336833Z     def data(times):
2026-04-11T12:05:56.8337346Z         data = np.random.rand(10, 10, _NT)
2026-04-11T12:05:56.8337910Z         lons = np.linspace(0, 11, 10)
2026-04-11T12:05:56.8338491Z         lats = np.linspace(0, 20, 10)
2026-04-11T12:05:56.8338959Z >       return xr.DataArray(
2026-04-11T12:05:56.8340025Z             data, coords=[lons, lats, times], dims=["lon", "lat", "time"], name="data"
2026-04-11T12:05:56.8340972Z         )
2026-04-11T12:05:56.8341103Z
2026-04-11T12:05:56.8341382Z /home/runner/work/xarray/xarray/xarray/tests/test_accessor_dt.py:424:
2026-04-11T12:05:56.8341816Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2026-04-11T12:05:56.8342337Z /home/runner/work/xarray/xarray/xarray/core/dataarray.py:463: in __init__
2026-04-11T12:05:56.8343168Z     coords, dims = _infer_coords_and_dims(data.shape, coords, dims)
2026-04-11T12:05:56.8343642Z                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2026-04-11T12:05:56.8344295Z /home/runner/work/xarray/xarray/xarray/core/dataarray.py:189: in _infer_coords_and_dims
2026-04-11T12:05:56.8345185Z     var = as_variable(coord, name=dim, auto_convert=False)
2026-04-11T12:05:56.8345811Z           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2026-04-11T12:05:56.8346298Z /home/runner/work/xarray/xarray/xarray/core/variable.py:166: in as_variable
2026-04-11T12:05:56.8346818Z     data: T_DuckArray = as_compatible_data(obj)
2026-04-11T12:05:56.8347207Z                         ^^^^^^^^^^^^^^^^^^^^^^^
2026-04-11T12:05:56.8347713Z /home/runner/work/xarray/xarray/xarray/core/variable.py:317: in as_compatible_data
2026-04-11T12:05:56.8348250Z     data = _possibly_convert_objects(data)
2026-04-11T12:05:56.8348616Z            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2026-04-11T12:05:56.8348922Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2026-04-11T12:05:56.8349148Z
2026-04-11T12:05:56.8349357Z values = array([cftime.Datetime360Day(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
2026-04-11T12:05:56.8349818Z        cftime.Datetime360Day(2000, 1, 1, 1, ... 0, has_year_zero=True),
2026-04-11T12:05:56.8350231Z        cftime.Datetime360Day(2000, 1, 5, 3, 0, 0, 0, has_year_zero=True)],
2026-04-11T12:05:56.8350563Z       dtype=object)
2026-04-11T12:05:56.8350676Z
2026-04-11T12:05:56.8350999Z     def _possibly_convert_objects(values):
2026-04-11T12:05:56.8351656Z         """Convert object arrays into datetime64 and timedelta64 according
2026-04-11T12:05:56.8352273Z         to the pandas convention.  For backwards compat, as of 3.0.0 pandas,
2026-04-11T12:05:56.8352826Z         object dtype inputs are cast to strings by `pandas.Series`
2026-04-11T12:05:56.8353417Z         but we output them as object dtype with the input metadata preserved as well.
2026-04-11T12:05:56.8353850Z
2026-04-11T12:05:56.8354059Z
2026-04-11T12:05:56.8354305Z         * datetime.datetime
2026-04-11T12:05:56.8354614Z         * datetime.timedelta
2026-04-11T12:05:56.8354917Z         * pd.Timestamp
2026-04-11T12:05:56.8355483Z         * pd.Timedelta
2026-04-11T12:05:56.8355769Z         """
2026-04-11T12:05:56.8356201Z         as_series = pd.Series(values.ravel(), copy=False)
2026-04-11T12:05:56.8356743Z         result = np.asarray(as_series).reshape(values.shape)
2026-04-11T12:05:56.8357250Z         if not result.flags.writeable:
2026-04-11T12:05:56.8357897Z             # GH8843, pandas copy-on-write mode creates read-only arrays by default
2026-04-11T12:05:56.8358409Z             try:
2026-04-11T12:05:56.8358793Z                 result.flags.writeable = True
2026-04-11T12:05:56.8359255Z             except ValueError:
2026-04-11T12:05:56.8359646Z                 result = result.copy()
2026-04-11T12:05:56.8360256Z         # For why we need this behavior: https://github.com/pandas-dev/pandas/issues/61938
2026-04-11T12:05:56.8360893Z         # Object datatype inputs that are strings
2026-04-11T12:05:56.8361504Z         # will be converted to strings by `pandas.Series`, and as of 3.0.0, lose
2026-04-11T12:05:56.8362395Z         # `dtype.metadata`.  If the roundtrip back to numpy in this function yields an
2026-04-11T12:05:56.8363073Z         # object array again, the dtype.metadata will be preserved.
2026-04-11T12:05:56.8363636Z         if (
2026-04-11T12:05:56.8364086Z             result.dtype.kind == "O"
2026-04-11T12:05:56.8364714Z             and values.dtype.kind == "O"
2026-04-11T12:05:56.8365765Z             and Version(pd.__version__) >= Version("3.0.0dev0")
2026-04-11T12:05:56.8366289Z         ):
2026-04-11T12:05:56.8366582Z >           result.dtype = values.dtype
2026-04-11T12:05:56.8366895Z             ^^^^^^^^^^^^
2026-04-11T12:05:56.8367410Z E           DeprecationWarning: Setting the dtype on a NumPy array has been deprecated in NumPy 2.4.
2026-04-11T12:05:56.8368127Z E           Instead of changing the dtype on an array x, create a new array with x.view(new_dtype)
2026-04-11T12:05:56.8368485Z
2026-04-11T12:05:56.8368800Z /home/runner/work/xarray/xarray/xarray/core/variable.py:241: DeprecationWarning

The switch to non-generic np.datetime64("NaT") values is not strictly necessary at this time, but it's not clear whether generic np.datetime("NaT") values will be deprecated at some point too, so I figured we might as well switch away from using those as well. It shouldn't have any impact on behavior.