Merge pull request #1215 from PCMDI/feature/1120_mov_fig

New MoV plot: multi panel plot

pcmdi_metrics/variability_mode/lib/__init__.py

@@ -33,5 +33,5 @@
     variability_metrics_to_json,
     write_nc_output,
 )
-from .plot_map import plot_map  # noqa
+from .plot_map import plot_map, plot_map_multi_panel  # noqa
 from .north_test import north_test

pcmdi_metrics/variability_mode/lib/eof_analysis.py

@@ -115,10 +115,14 @@ def eof_analysis_get_variance_mode(
             pc_Nth *= -1.0
 
         # Supplement NetCDF attributes
+        eof_Nth.attrs["variable"] = data_var
+        eof_Nth.attrs["eof_mode"] = n + 1
         frac_Nth.attrs["units"] = "ratio"
         pc_Nth.attrs[
             "comment"
         ] = f"Non-scaled time series for principal component of {eofn}th variance mode"
+        pc_Nth.attrs["variable"] = data_var
+        pc_Nth.attrs["eof_mode"] = n + 1
 
         # append to lists for returning
         eof_list.append(eof_Nth)
@@ -258,6 +262,14 @@ def linear_regression_on_globe_for_teleconnection(
 
     eof_lr = (slope * factor) + intercept
 
+    eof_lr.attrs["variable"] = data_var
+    eof_lr.attrs["description"] = "linear regression on global field for teleconnection"
+    eof_lr.attrs[
+        "comment"
+    ] = "Reconstructed EOF pattern with teleconnection considerations"
+    if "eof_mode" in pc.attrs:
+        eof_lr.attrs["eof_mode"] = pc.attrs["eof_mode"]
+
     debug_print("linear regression done", debug)
 
     return eof_lr, slope, intercept
@@ -346,10 +358,12 @@ def gain_pseudo_pcs(
         pseudo_pcs = solver.projectField(
             field_to_be_projected, neofs=eofn, eofscaling=0
         )
+        pseudo_pcs.attrs["comment"] = "Non-scaled pseudo principal components"
     else:
         pseudo_pcs = solver.projectField(
             field_to_be_projected, neofs=eofn, eofscaling=1
         )
+        pseudo_pcs.attrs["comment"] = "Scaled pseudo principal components"
     # Get CBF PC (pseudo pcs in the code) for given eofs
     pseudo_pcs = pseudo_pcs[:, eofn - 1]
     # Arbitrary sign control, attempt to make all plots have the same sign

pcmdi_metrics/variability_mode/lib/lib_variability_mode.py

@@ -33,7 +33,9 @@ def tree():
     return defaultdict(tree)
 
 
-def write_nc_output(output_file_name, eofMap, pc, frac, slopeMap, interceptMap):
+def write_nc_output(
+    output_file_name, eofMap, pc, frac, slopeMap, interceptMap, identifier=None
+):
     # Create a dataset
     ds = xr.Dataset(
         {
@@ -46,6 +48,23 @@ def write_nc_output(output_file_name, eofMap, pc, frac, slopeMap, interceptMap):
             ),  # single number having no axis
         }
     )
+    # Add global attributes
+    ds.attrs[
+        "title"
+    ] = "PCMDI Metrics Package Extratropical Modes of Variability diagnostics"
+    ds.attrs["author"] = "PCMDI"
+    ds.attrs["contact"] = "[email protected]"
+    ds.attrs["creation_date"] = strftime("%Y-%m-%d %H:%M:%S", gmtime())
+    ds.attrs[
+        "references"
+    ] = """
+    Lee, J., K. Sperber, P. Gleckler, C. Bonfils, and K. Taylor, 2019: Quantifying the Agreement Between Observed and Simulated Extratropical Modes of Interannual Variability. Climate Dynamics, 52, 4057-4089, doi: 10.1007/s00382-018-4355-4,
+    Lee, J., K. Sperber, P. Gleckler, K. Taylor, and C. Bonfils, 2021: Benchmarking performance changes in the simulation of extratropical modes of variability across CMIP generations. Journal of Climate, 34, 6945–6969, doi: 10.1175/JCLI-D-20-0832.1,
+    Lee, J., P. J. Gleckler, M.-S. Ahn, A. Ordonez, P. Ullrich, K. R. Sperber, K. E. Taylor, Y. Y. Planton, E. Guilyardi, P. Durack, C. Bonfils, M. D. Zelinka, L.-W. Chao, B. Dong, C. Doutriaux, C. Zhang, T. Vo, J. Boutte, M. F. Wehner, A. G. Pendergrass, D. Kim, Z. Xue, A. T. Wittenberg, and J. Krasting, 2024: Systematic and Objective Evaluation of Earth System Models: PCMDI Metrics Package (PMP) version 3. Geoscientific Model Development, 17, 3919–3948, doi: 10.5194/gmd-17-3919-2024
+    """
+    if identifier is not None:
+        ds.attrs["identifier"] = identifier
+    # Save the dataset to a netcdf file
     ds.to_netcdf(output_file_name + ".nc")
     ds.close()