Merge branch 'main' into feature/1084_lee1043_MJO-xcdat

docs/index.rst

@@ -39,7 +39,7 @@ such as datasets from the `obs4MIPs`_ project.
 
 References
 ==========
-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 (accepted, publication in progress) [`preprint<https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2720/>`_].
+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, https://doi.org/10.5194/gmd-17-3919-2024.
 
 Gleckler et al. (2016), A more powerful reality test for climate models, Eos, 97, `doi:10.1029/2016EO051663 <https://eos.org/science-updates/a-more-powerful-reality-test-for-climate-models>`_.
 

docs/metrics.rst

@@ -21,3 +21,4 @@ A suite of demo scripts and interactive Jupyter notebooks are provided with `thi
  metrics_precip-distribution
  metrics_subdaily-precipitation
  metrics_sea_ice
+ Cloud Feedbacks <https://github.com/PCMDI/pcmdi_metrics/blob/main/pcmdi_metrics/cloud_feedback/README.md>

pcmdi_metrics/sea_ice/sea_ice_driver.py

@@ -228,14 +228,14 @@
  end_year = meyear
 
  real_clim = {
- "arctic": {"model_mean": None},
- "ca": {"model_mean": None},
- "na": {"model_mean": None},
- "np": {"model_mean": None},
- "antarctic": {"model_mean": None},
- "sp": {"model_mean": None},
- "sa": {"model_mean": None},
- "io": {"model_mean": None},
+ "arctic": {"model_mean": {}},
+ "ca": {"model_mean": {}},
+ "na": {"model_mean": {}},
+ "np": {"model_mean": {}},
+ "antarctic": {"model_mean": {}},
+ "sp": {"model_mean": {}},
+ "sa": {"model_mean": {}},
+ "io": {"model_mean": {}},
  }
  real_mean = {
  "arctic": {"model_mean": 0},
@@ -309,7 +309,15 @@
  "%(model_version)": model,
  "%(realization)": run,
  }
- test_data_full_path = os.path.join(test_data_path, filename_template)
+ test_data_tmp = lib.replace_multi(test_data_path, tags)
+ if "*" in test_data_tmp:
+ # Get the most recent version for last wildcard
+ ind = test_data_tmp.split("/")[::-1].index("*")
+ tmp1 = "/".join(test_data_tmp.split("/")[0:-ind])
+ globbed = glob.glob(tmp1)
+ globbed.sort()
+ test_data_tmp = globbed[-1]
+ test_data_full_path = os.path.join(test_data_tmp, filename_template)
  test_data_full_path = lib.replace_multi(test_data_full_path, tags)
  test_data_full_path = glob.glob(test_data_full_path)
  test_data_full_path.sort()
@@ -365,29 +373,20 @@
  # Running sum of all realizations
  for rgn in clims:
  real_clim[rgn][run] = clims[rgn]
- if real_clim[rgn]["model_mean"] is None:
- real_clim[rgn]["model_mean"] = clims[rgn]
- else:
- real_clim[rgn]["model_mean"][var] = (
- real_clim[rgn]["model_mean"][var] + clims[rgn][var]
- )
  real_mean[rgn][run] = means[rgn]
- real_mean[rgn]["model_mean"] = (
- real_mean[rgn]["model_mean"] + means[rgn]
- )
 
  print("\n-------------------------------------------")
  print("Calculating model regional average metrics \nfor ", model)
  print("--------------------------------------------")
  for rgn in real_clim:
  print(rgn)
  # Get model mean
- real_clim[rgn]["model_mean"][var] = real_clim[rgn]["model_mean"][
- var
- ] / len(list_of_runs)
- real_mean[rgn]["model_mean"] = real_mean[rgn]["model_mean"] / len(
- list_of_runs
+ datalist = [real_clim[rgn][r][var].data for r in list_of_runs]
+ real_clim[rgn]["model_mean"][var] = np.nanmean(
+ np.array(datalist), axis=0
  )
+ datalist = [real_mean[rgn][r] for r in list_of_runs]
+ real_mean[rgn]["model_mean"] = np.nanmean(np.array(datalist))
 
  for run in real_clim[rgn]:
  # Set up metrics dictionary