diff --git a/xarray/tests/test_array_api.py b/xarray/tests/test_array_api.py
index 47e38013662..c273260d7dd 100644
--- a/xarray/tests/test_array_api.py
+++ b/xarray/tests/test_array_api.py
@@ -51,6 +51,8 @@ def test_aggregation_skipna(arrays) -> None:
     assert_equal(actual, expected)
 
 
+# casting nan warns
+@pytest.mark.filterwarnings("ignore:invalid value encountered in cast")
 def test_astype(arrays) -> None:
     np_arr, xp_arr = arrays
     expected = np_arr.astype(np.int64)
diff --git a/xarray/tests/test_backends.py b/xarray/tests/test_backends.py
index b3ded709fc0..5f693e7a990 100644
--- a/xarray/tests/test_backends.py
+++ b/xarray/tests/test_backends.py
@@ -4185,7 +4185,7 @@ def test_phony_dims_warning(self) -> None:
                         fx.create_dataset(k, data=v)
             with pytest.warns(UserWarning, match="The 'phony_dims' kwarg"):
                 with xr.open_dataset(tmp_file, engine="h5netcdf", group="bar") as ds:
-                    assert ds.dims == {
+                    assert ds.sizes == {
                         "phony_dim_0": 5,
                         "phony_dim_1": 5,
                         "phony_dim_2": 5,
@@ -4300,7 +4300,8 @@ def test_open_fileobj(self) -> None:
             # `raises_regex`?). Ref https://github.com/pydata/xarray/pull/5191
             with open(tmp_file, "rb") as f:
                 f.seek(8)
-                open_dataset(f)
+                with open_dataset(f):  # ensure file gets closed
+                    pass
 
 
 @requires_h5netcdf
@@ -6587,11 +6588,11 @@ def test_h5netcdf_storage_options() -> None:
         ds2.to_netcdf(f2, engine="h5netcdf")
 
         files = [f"file://{f}" for f in [f1, f2]]
-        ds = xr.open_mfdataset(
+        with xr.open_mfdataset(
             files,
             engine="h5netcdf",
             concat_dim="time",
             combine="nested",
             storage_options={"skip_instance_cache": False},
-        )
-        assert_identical(xr.concat([ds1, ds2], dim="time"), ds)
+        ) as ds:
+            assert_identical(xr.concat([ds1, ds2], dim="time"), ds)
diff --git a/xarray/tests/test_computation.py b/xarray/tests/test_computation.py
index 1d80d874df0..8ca9d2bbbb5 100644
--- a/xarray/tests/test_computation.py
+++ b/xarray/tests/test_computation.py
@@ -634,6 +634,7 @@ def test_apply_groupby_add() -> None:
         add(data_array.groupby("y"), data_array.groupby("x"))
 
 
+@pytest.mark.filterwarnings("ignore:Duplicate dimension names present")
 def test_unified_dim_sizes() -> None:
     assert unified_dim_sizes([xr.Variable((), 0)]) == {}
     assert unified_dim_sizes([xr.Variable("x", [1]), xr.Variable("x", [1])]) == {"x": 1}
@@ -646,9 +647,9 @@ def test_unified_dim_sizes() -> None:
         exclude_dims={"z"},
     ) == {"x": 1, "y": 2}
 
-    # duplicate dimensions
-    with pytest.raises(ValueError):
-        unified_dim_sizes([xr.Variable(("x", "x"), [[1]])])
+    with pytest.raises(ValueError, match="broadcasting cannot handle"):
+        with pytest.warns(UserWarning, match="Duplicate dimension names"):
+            unified_dim_sizes([xr.Variable(("x", "x"), [[1]])])
 
     # mismatched lengths
     with pytest.raises(ValueError):
diff --git a/xarray/tests/test_dataset.py b/xarray/tests/test_dataset.py
index 0ab162cb214..bdae9daf758 100644
--- a/xarray/tests/test_dataset.py
+++ b/xarray/tests/test_dataset.py
@@ -6007,6 +6007,8 @@ def test_dataset_number_math(self) -> None:
         actual += 0
         assert_identical(ds, actual)
 
+    # casting nan warns
+    @pytest.mark.filterwarnings("ignore:invalid value encountered in cast")
     def test_unary_ops(self) -> None:
         ds = self.make_example_math_dataset()
 
diff --git a/xarray/tests/test_variable.py b/xarray/tests/test_variable.py
index b1d5a09efcf..388f51bc568 100644
--- a/xarray/tests/test_variable.py
+++ b/xarray/tests/test_variable.py
@@ -2952,7 +2952,8 @@ def test_from_sparse(self, Var):
         import sparse
 
         arr = np.diagflat([1, 2, 3])
-        sparr = sparse.COO(coords=[[0, 1, 2], [0, 1, 2]], data=[1, 2, 3])
+        coords = np.array([[0, 1, 2], [0, 1, 2]])
+        sparr = sparse.COO(coords=coords, data=[1, 2, 3], shape=(3, 3))
         v = Variable(["x", "y"], sparr)
 
         assert_identical(v.as_numpy(), Variable(["x", "y"], arr))