Remove assertion that bias adjustment precipitation factor be between 0.5 and 2

Closes #118. 
This PR returns the GCM precipitation bias adjustment factors to `run_simulation.py` and removed previous assertions that the bias-adjusted GCM precipitation divided by the reference climate precipitation not differ by a factor >2 for the same time period.

A warning is now raised in `run_simulation.py` if the factor differs by >2x, but the simulation will still run.

Also cleaned up relict variable naming that was in place to handle `ref_spinupyears` and `gcm_spinupyears`.

Author: btobers

pygem/bin/op/compress_gdirs.py

@@ -16,12 +16,12 @@
 from oggm import utils, workflow
 
 # pygem imports
-import pygem.setup.config as config
+from pygem.setup.config import ConfigManager
 
-# check for config
-config.ensure_config()
+# instantiate ConfigManager
+config_manager = ConfigManager()
 # read the config
-pygem_prms = config.read_config()
+pygem_prms = config_manager.read_config()
 
 # Initialize OGGM subprocess
 cfg.initialize(logging_level='WARNING')

pygem/bin/run/run_inversion.py

@@ -6,12 +6,12 @@
 import pandas as pd
 
 # pygem imports
-import pygem.setup.config as config
+from pygem.setup.config import ConfigManager
 
-# check for config
-config.ensure_config()
+# instantiate ConfigManager
+config_manager = ConfigManager()
 # read the config
-pygem_prms = config.read_config()
+pygem_prms = config_manager.read_config()
 from oggm import cfg, tasks, workflow
 
 import pygem.pygem_modelsetup as modelsetup

pygem/bin/run/run_simulation.py

@@ -23,6 +23,7 @@
 import os
 import sys
 import time
+import warnings
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -457,6 +458,7 @@ def run(list_packed_vars):
         gcm_elev_adj = gcm_elev
         gcm_temp_adj = gcm_temp[:, sim_idx_start:]
         gcm_prec_adj = gcm_prec[:, sim_idx_start:]
+        gcm_prec_biasadj_frac = np.ones(gcm_prec_adj.shape[0])
     # Bias correct based on reference climate data
     else:
         # OPTION 1: Adjust temp using Huss and Hock (2015), prec similar but addresses for variance and outliers
@@ -472,14 +474,16 @@ def run(list_packed_vars):
                 args.ref_startyear,
             )
             # Precipitation bias correction
-            gcm_prec_adj, gcm_elev_adj = gcmbiasadj.prec_biasadj_opt1(
-                ref_prec,
-                ref_elev,
-                gcm_prec,
-                dates_table_ref,
-                dates_table_full,
-                args.sim_startyear,
-                args.ref_startyear,
+            gcm_prec_adj, gcm_elev_adj, gcm_prec_biasadj_frac = (
+                gcmbiasadj.prec_biasadj_opt1(
+                    ref_prec,
+                    ref_elev,
+                    gcm_prec,
+                    dates_table_ref,
+                    dates_table_full,
+                    args.sim_startyear,
+                    args.ref_startyear,
+                )
             )
         # OPTION 2: Adjust temp and prec using Huss and Hock (2015)
         elif args.option_bias_adjustment == 2:
@@ -494,12 +498,14 @@ def run(list_packed_vars):
                 args.ref_startyear,
             )
             # Precipitation bias correction
-            gcm_prec_adj, gcm_elev_adj = gcmbiasadj.prec_biasadj_HH2015(
-                ref_prec,
-                ref_elev,
-                gcm_prec,
-                dates_table_ref,
-                dates_table_full,
+            gcm_prec_adj, gcm_elev_adj, gcm_prec_biasadj_frac = (
+                gcmbiasadj.prec_biasadj_HH2015(
+                    ref_prec,
+                    ref_elev,
+                    gcm_prec,
+                    dates_table_ref,
+                    dates_table_full,
+                )
             )
         # OPTION 3: Adjust temp and prec using quantile delta mapping, Cannon et al. (2015)
         elif args.option_bias_adjustment == 3:
@@ -524,6 +530,7 @@ def run(list_packed_vars):
                 args.sim_startyear,
                 args.ref_startyear,
             )
+            gcm_prec_biasadj_frac = np.ones(gcm_prec_adj.shape[0])
 
     # assert that the gcm_elev_adj is not None
     assert gcm_elev_adj is not None, 'No GCM elevation data'
@@ -640,6 +647,13 @@ def run(list_packed_vars):
                 'prec': gcm_prec_adj[glac, :],
                 'lr': gcm_lr[glac, :],
             }
+            fact = gcm_prec_biasadj_frac[glac]
+            if fact < 0.5 or fact > 2:
+                warnings.warn(
+                    f'Bias-adjusted GCM precipitation for {glacier_str} differs from that of the refernce climate data by a factor greater than 2x ({round(fact, 2)})',
+                    Warning,
+                    stacklevel=2,
+                )
             gdir.dates_table = dates_table
 
             glacier_area_km2 = fls[0].widths_m * fls[0].dx_meter / 1e6

pygem/bin/run/run_spinup.py

@@ -7,12 +7,12 @@
 import pandas as pd
 
 # pygem imports
-import pygem.setup.config as config
+from pygem.setup.config import ConfigManager
 
-# check for config
-config.ensure_config()
+# instantiate ConfigManager
+config_manager = ConfigManager()
 # read the config
-pygem_prms = config.read_config()
+pygem_prms = config_manager.read_config()
 from oggm import tasks, workflow
 
 import pygem.pygem_modelsetup as modelsetup