Preserve legacy Xarray merge settings

e3sm_diags/__init__.py

@@ -11,3 +11,12 @@
 os.environ["OPENBLAS_NUM_THREADS"] = "1"
 os.environ["OMP_NUM_THREADS"] = "1"
 os.environ["MKL_NUM_THREADS"] = "1"
+
+# Settings to preserve legacy Xarray behavior when merging datasets.
+# See https://xarray.pydata.org/en/stable/user-guide/io.html#combining-multiple-files
+# and https://xarray.pydata.org/en/stable/whats-new.html#id14
+LEGACY_XARRAY_MERGE_KWARGS = {
+    # "override", "exact" are the new defaults as of Xarray v2025.08.0
+    "compat": "no_conflicts",
+    "join": "outer",
+}

e3sm_diags/driver/mp_partition_driver.py

@@ -16,7 +16,7 @@
 import xarray as xr
 from scipy.stats import binned_statistic
 
-from e3sm_diags import INSTALL_PATH
+from e3sm_diags import INSTALL_PATH, LEGACY_XARRAY_MERGE_KWARGS
 from e3sm_diags.driver.utils.dataset_xr import Dataset
 from e3sm_diags.logger import _setup_child_logger
 from e3sm_diags.plot.mp_partition_plot import plot
@@ -60,19 +60,27 @@ def compute_lcf(cice, cliq, temp, landfrac):
 
 
 def run_diag(parameter: MPpartitionParameter) -> MPpartitionParameter:
-    """Runs the mixed-phase partition/T5050 diagnostic.
-
-    :param parameter: Parameters for the run
-    :type parameter: CoreParameter
-    :raises ValueError: Invalid run type
-    :return: Parameters for the run
-    :rtype: CoreParameter
+    """
+    Runs the mixed-phase partition/T5050 diagnostic.
+
+    Parameters
+    ----------
+    parameter : CoreParameter
+        Parameters for the run.
+
+    Raises
+    ------
+    ValueError
+        If the run type is invalid.
+
+    Returns
+    -------
+    CoreParameter
+        Parameters for the run.
     """
     run_type = parameter.run_type
     season = "ANN"
 
-    # Read reference data first
-
     benchmark_data_path = os.path.join(
         INSTALL_PATH,
         "control_runs",
@@ -82,35 +90,20 @@ def run_diag(parameter: MPpartitionParameter) -> MPpartitionParameter:
     with open(benchmark_data_path, "r") as myfile:
         lcf_file = myfile.read()
 
-    # parse file
     metrics_dict = json.loads(lcf_file)
 
     test_data = Dataset(parameter, data_type="test")
-    # test = test_data.get_timeseries_variable("LANDFRAC")
-    # print(dir(test))
-    # landfrac = test_data.get_timeseries_variable("LANDFRAC")(cdutil.region.domain(latitude=(-70.0, -30, "ccb")))
-    # temp = test_data.get_timeseries_variable("T")(cdutil.region.domain(latitude=(-70.0, -30, "ccb")))
-    # cice = test_data.get_timeseries_variable("CLDICE")(cdutil.region.domain(latitude=(-70.0, -30, "ccb")))
-    # cliq = test_data.get_timeseries_variable("CLDLIQ")(cdutil.region.domain(latitude=(-70.0, -30, "ccb")))
-
     test_data_path = parameter.test_data_path
-    start_year = parameter.test_start_yr
-    end_year = parameter.test_end_yr
+    start_year = int(parameter.test_start_yr)
+
+    end_year = int(parameter.test_end_yr)
+
     # TODO the time subsetting and variable derivation should be replaced during cdat revamp
     try:
-        # xr.open_mfdataset() can accept an explicit list of files.
-        landfrac = xr.open_mfdataset(glob.glob(f"{test_data_path}/LANDFRAC_*")).sel(
-            lat=slice(-70, -30), time=slice(f"{start_year}-01-01", f"{end_year}-12-31")
-        )["LANDFRAC"]
-        temp = xr.open_mfdataset(glob.glob(f"{test_data_path}/T_*.nc")).sel(
-            lat=slice(-70, -30), time=slice(f"{start_year}-01-01", f"{end_year}-12-31")
-        )["T"]
-        cice = xr.open_mfdataset(glob.glob(f"{test_data_path}/CLDICE_*.nc")).sel(
-            lat=slice(-70, -30), time=slice(f"{start_year}-01-01", f"{end_year}-12-31")
-        )["CLDICE"]
-        cliq = xr.open_mfdataset(glob.glob(f"{test_data_path}/CLDLIQ_*.nc")).sel(
-            lat=slice(-70, -30), time=slice(f"{start_year}-01-01", f"{end_year}-12-31")
-        )["CLDLIQ"]
+        landfrac = _open_mfdataset(test_data_path, "LANDFRAC", start_year, end_year)
+        temp = _open_mfdataset(test_data_path, "T", start_year, end_year)
+        cice = _open_mfdataset(test_data_path, "CLDICE", start_year, end_year)
+        cliq = _open_mfdataset(test_data_path, "CLDLIQ", start_year, end_year)
     except OSError:
         logger.info(
             f"No files to open for variables within {start_year} and {end_year} from {test_data_path}."
@@ -126,46 +119,19 @@ def run_diag(parameter: MPpartitionParameter) -> MPpartitionParameter:
 
     if run_type == "model-vs-model":
         ref_data = Dataset(parameter, data_type="ref")
-
         ref_data_path = parameter.reference_data_path
-        start_year = parameter.ref_start_yr
-        end_year = parameter.ref_end_yr
-        # xr.open_mfdataset() can accept an explicit list of files.
+
         try:
-            landfrac = xr.open_mfdataset(glob.glob(f"{ref_data_path}/LANDFRAC_*")).sel(
-                lat=slice(-70, -30),
-                time=slice(f"{start_year}-01-01", f"{end_year}-12-31"),
-            )["LANDFRAC"]
-            temp = xr.open_mfdataset(glob.glob(f"{ref_data_path}/T_*.nc")).sel(
-                lat=slice(-70, -30),
-                time=slice(f"{start_year}-01-01", f"{end_year}-12-31"),
-            )["T"]
-            cice = xr.open_mfdataset(glob.glob(f"{ref_data_path}/CLDICE_*.nc")).sel(
-                lat=slice(-70, -30),
-                time=slice(f"{start_year}-01-01", f"{end_year}-12-31"),
-            )["CLDICE"]
-            cliq = xr.open_mfdataset(glob.glob(f"{ref_data_path}/CLDLIQ_*.nc")).sel(
-                lat=slice(-70, -30),
-                time=slice(f"{start_year}-01-01", f"{end_year}-12-31"),
-            )["CLDLIQ"]
+            landfrac = _open_mfdataset(ref_data_path, "LANDFRAC", start_year, end_year)
+            temp = _open_mfdataset(ref_data_path, "T", start_year, end_year)
+            cice = _open_mfdataset(ref_data_path, "CLDICE", start_year, end_year)
+            cliq = _open_mfdataset(ref_data_path, "CLDLIQ", start_year, end_year)
         except OSError:
             logger.info(
                 f"No files to open for variables within {start_year} and {end_year} from {ref_data_path}."
             )
             raise
 
-        # landfrac = ref_data.get_timeseries_variable("LANDFRAC")(
-        #    cdutil.region.domain(latitude=(-70.0, -30, "ccb"))
-        # )
-        # temp = ref_data.get_timeseries_variable("T")(
-        #    cdutil.region.domain(latitude=(-70.0, -30, "ccb"))
-        # )
-        # cice = ref_data.get_timeseries_variable("CLDICE")(
-        #    cdutil.region.domain(latitude=(-70.0, -30, "ccb"))
-        # )
-        # cliq = ref_data.get_timeseries_variable("CLDLIQ")(
-        #    cdutil.region.domain(latitude=(-70.0, -30, "ccb"))
-        # )
         parameter.ref_name_yrs = ref_data.get_name_yrs_attr(season)
         metrics_dict["ref"] = {}
         metrics_dict["ref"]["T"], metrics_dict["ref"]["LCF"] = compute_lcf(
@@ -177,3 +143,47 @@ def run_diag(parameter: MPpartitionParameter) -> MPpartitionParameter:
     plot(metrics_dict, parameter)
 
     return parameter
+
+
+def _open_mfdataset(
+    data_path: str, var: str, start_year: int, end_year: int
+) -> xr.DataArray:
+    """
+    Open multiple NetCDF files as a single xarray Dataset and subset by time
+    and latitude.
+
+    This function reads multiple NetCDF files matching the specified variable
+    name and combines them into a single xarray Dataset. The data is then
+    subsetted based on the specified time range and latitude bounds.
+
+    Parameters
+    ----------
+    data_path : str
+        The path to the directory containing the NetCDF files.
+    var : str
+        The variable name to match in the file pattern.
+    start_year : int
+        The starting year for the time subsetting.
+    end_year : int
+        The ending year for the time subsetting.
+
+    Returns
+    -------
+    xr.DataArray
+        The subsetted DataArray for the specified variable, filtered by time
+        and latitude.
+    """
+    file_pattern = f"{data_path}/{var}_*.nc"
+    ds = xr.open_mfdataset(
+        glob.glob(file_pattern),
+        data_vars="minimal",
+        **LEGACY_XARRAY_MERGE_KWARGS,  # type: ignore[ arg-type ]
+    )
+
+    ds_sub = ds.sel(
+        lat=slice(-70, -30), time=slice(f"{start_year}-01-01", f"{end_year}-12-31")
+    )
+
+    da_var = ds_sub[var]
+
+    return da_var

e3sm_diags/driver/tropical_subseasonal_driver.py

@@ -244,6 +244,7 @@ def wf_analysis(x, **kwargs):
             background.rename("spec_background"),
         ],
         compat="override",
+        join="outer",
     )
     spec_all = spec.drop("component")
     spec_all["spec_raw_sym"].attrs = {"component": "symmetric", "type": "raw"}

e3sm_diags/driver/utils/dataset_xr.py

@@ -24,6 +24,7 @@
 import xarray as xr
 import xcdat as xc
 
+from e3sm_diags import LEGACY_XARRAY_MERGE_KWARGS
 from e3sm_diags.derivations.derivations import (
     DERIVED_VARIABLES,
     FUNC_NEEDS_TARGET_VAR,
@@ -1159,7 +1160,7 @@ def _get_dataset_with_source_vars(self, vars_to_get: tuple[str, ...]) -> xr.Data
             ds = self._get_time_series_dataset_obj(var)
             datasets.append(ds)
 
-        ds = xr.merge(datasets)
+        ds = xr.merge(datasets, **LEGACY_XARRAY_MERGE_KWARGS)  # type: ignore[ arg-type ]
         ds = squeeze_time_dim(ds)
 
         return ds
@@ -1640,7 +1641,7 @@ def _get_land_sea_mask_dataset(self, season: ClimoFreq) -> xr.Dataset | None:
                 datasets.append(ds_ocn)
 
         if len(datasets) == 2:
-            return xr.merge(datasets)
+            return xr.merge(datasets, **LEGACY_XARRAY_MERGE_KWARGS)  # type: ignore[ arg-type ]
 
         return None