Adding 0.8 1.2 GWLs into cava_data options

climakitae/core/constants.py

@@ -1,21 +1,42 @@
 # constants.py
 """This module defines constants across the codebase"""
 
+# global warming levels available on AE
 WARMING_LEVELS = [0.8, 1.2, 1.5, 2.0, 2.5, 3.0, 4.0]
+
+# shared socioeconomic pathways (IPCC)
 SSPS = [
     "SSP 2-4.5",
     "SSP 3-7.0",
     "SSP 5-8.5",
 ]
+
+# WRF models that have a-priori bias adjustment
 WRF_BA_MODELS = [
     "WRF_EC-Earth3_r1i1p1f1",
     "WRF_MPI-ESM1-2-HR_r3i1p1f1",
     "WRF_TaiESM1_r1i1p1f1",
     "WRF_MIROC6_r1i1p1f1",
     "WRF_EC-Earth3-Veg_r1i1p1f1",
 ]
+
+# WRF models that do not have a-priori bias adjustment
 NON_WRF_BA_MODELS = [
     "WRF_FGOALS-g3_r1i1p1f1",
     "WRF_CNRM-ESM2-1_r1i1p1f2",
     "WRF_CESM2_r11i1p1f1",
 ]
+
+# WRF models that do not reach 0.8°C GWL
+WRF_NO_0PT8_GWL_MODELS = ["WRF_EC-Earth3-Veg_r1i1p1f1_historical+ssp370"]
+
+# LOCA models that do not reach 0.8°C GWL
+LOCA_NO_0PT8_GWL_MODELS = [
+    "LOCA2_EC-Earth3_r4i1p1f1_historical+ssp245",
+    "LOCA2_EC-Earth3_r4i1p1f1_historical+ssp370",
+    "LOCA2_EC-Earth3_r4i1p1f1_historical+ssp585",
+    "LOCA2_EC-Earth3-Veg_r3i1p1f1_historical+ssp245",
+    "LOCA2_EC-Earth3-Veg_r3i1p1f1_historical+ssp370",
+    "LOCA2_EC-Earth3-Veg_r3i1p1f1_historical+ssp585",
+    "LOCA2_EC-Earth3-Veg_r5i1p1f1_historical+ssp245",
+]

climakitae/explore/vulnerability.py

@@ -13,7 +13,12 @@
 from climakitae.core.data_export import export
 from climakitae.core.data_interface import DataParameters
 from climakitae.core.data_load import load
-from climakitae.core.constants import WRF_BA_MODELS, SSPS
+from climakitae.core.constants import (
+    WRF_BA_MODELS,
+    SSPS,
+    WRF_NO_0PT8_GWL_MODELS,
+    LOCA_NO_0PT8_GWL_MODELS,
+)
 from climakitae.explore import warming_levels
 from climakitae.explore.threshold_tools import (
     get_block_maxima,
@@ -42,7 +47,7 @@ def _export_no_e(da, filename, format):
 
 
 def _filter_ba_models(data, downscaling_method, wrf_bias_adjust, historical_data):
-    """Filters the data for the bias adjusted simulations, if desired."""
+    """Filters the WRF data for the bias adjusted simulations, if desired."""
     if (
         downscaling_method == "Dynamical"
         and wrf_bias_adjust
@@ -58,6 +63,33 @@ def _filter_ba_models(data, downscaling_method, wrf_bias_adjust, historical_data
                 if any(s in sim for s in WRF_BA_MODELS)
             ]
         )
+        print(f"Filtering WRF data for bias-adjusted simulations.")
+    return data
+
+
+def _filter_hist_gwl_models(data, downscaling_method, approach, warming_level):
+    """Filters the data to remove simulation(s) that do not reach 0.8°C in GWL selection"""
+
+    # Filter for viable 0.8°C simulations
+    if downscaling_method == "Dynamical":
+        data = data.sel(
+            simulation=[
+                sim
+                for sim in data.simulation.values
+                if sim not in WRF_NO_0PT8_GWL_MODELS
+            ]
+        )
+    elif downscaling_method == "Statistical":
+        data = data.sel(
+            simulation=[
+                sim
+                for sim in data.simulation.values
+                if sim not in LOCA_NO_0PT8_GWL_MODELS
+            ]
+        )
+    print(
+        f"Not all {downscaling_method} simulations have data for {warming_level}°C. Removing invalid simulations. Please see Guidance materials for more information."
+    )  # guidance forthcoming
     return data
 
 
@@ -282,7 +314,7 @@ class CavaParams(param.Parameterized):
     )
     warming_level = param.ObjectSelector(
         default=1.5,
-        objects=[None, 1.5, 2.0, 2.5, 3.0],
+        objects=[None, 0.8, 1.2, 1.5, 2.0, 2.5, 3.0],
         doc="Warming level for analysis",
     )
     wrf_bias_adjust = param.Boolean(default=True, doc="Flag for WRF bias adjustment")
@@ -358,12 +390,14 @@ def validate_params(self):
 
         # Validating warming level inputs
         if self.approach == "Warming Level" and self.warming_level not in [
+            0.8,
+            1.2,
             1.5,
             2.0,
             2.5,
             3.0,
         ]:
-            errors.append("Warming level must be 1.5, 2.0, 2.5, or 3.0.")
+            errors.append("Warming level must be 0.8, 1.2, 1.5, 2.0, 2.5, or 3.0.")
 
         # Validating that only one of heat_idx_threshold, percentile, or one_in_x is passed
         if (
@@ -446,7 +480,7 @@ def get_names(self):
         elif self.approach == "Warming Level":
             wl_str = (
                 str(int(self.warming_level))
-                if self.warming_level not in [1.5, 2.5]
+                if self.warming_level not in [0.8, 1.2, 1.5, 2.5]
                 else str(self.warming_level).replace(".", "pt")
             )
             approach_str = f"{wl_str}degreeWL"
@@ -539,6 +573,8 @@ def cava_data(
         Type of historical data, default is "Historical Climate".
     ssp_data : str, optional
         Shared Socioeconomic Pathway data, default is "SSP 3-7.0".
+    warming_level : str, optional
+        Global warming levels, default is 1.5°C.
     metric_calc : str, optional
         Metric calculation type (e.g., 'mean', 'max', 'min') for supported metrics. Default is "max"
     heat_idx_threshold : float
@@ -636,6 +672,12 @@ def cava_data(
                 data_pts, downscaling_method, wrf_bias_adjust, historical_data
             )
 
+            if approach == "Warming Level" and warming_level == 0.8:
+                # Filter out inappropriate models for historical GWL baseline
+                data = _filter_hist_gwl_models(
+                    data, downscaling_method, approach, warming_level
+                )
+
         else:
             data_pts = []
             for idx, loc in input_locations.iterrows():
@@ -659,6 +701,11 @@ def cava_data(
                     data, downscaling_method, wrf_bias_adjust, historical_data
                 )
 
+                if approach == "Warming Level" and warming_level == 0.8:
+                    # Filter out inappropriate models for historical GWL baseline
+                    data = _filter_hist_gwl_models(
+                        data, downscaling_method, approach, warming_level
+                    )
                 data_pts.append(data)
 
         if historical_data == "Historical Reconstruction":

climakitae/explore/warming.py

@@ -48,7 +48,7 @@ class WarmingLevels:
 
     def __init__(self, **params):
         self.wl_params = WarmingLevelChoose()
-        # self.warming_levels = ["1.5", "2.0", "3.0", "4.0"]
+        # self.warming_levels = ["0.8", "1.2", "1.5", "2.0", "3.0", "4.0"]
 
     def find_warming_slice(self, level, gwl_times):
         """
@@ -79,6 +79,7 @@ def calculate(self):
         # manually reset to all SSPs, in case it was inadvertently changed by
         # temporarily have ['Dynamical','Statistical'] for downscaling_method
         self.wl_params.scenario_ssp = SSPS
+
         # Postage data and anomalies
         self.catalog_data = self.wl_params.retrieve()
         self.catalog_data = self.catalog_data.stack(all_sims=["simulation", "scenario"])
@@ -170,7 +171,7 @@ def clean_warm_data(warm_data):
     return warm_data
 
 
-def get_sliced_data(y, level, years, months=np.arange(1, 13), window=15, anom="Yes"):
+def get_sliced_data(y, level, years, months=np.arange(1, 13), window=15, anom="No"):
     """Calculating warming level anomalies.
 
     Parameters
@@ -288,7 +289,7 @@ class WarmingLevelChoose(DataParameters):
     anom = param.Selector(
         default="Yes",
         objects=["Yes", "No"],
-        doc="Return an anomaly \n(difference from historical reference period)?",
+        doc="Return a delta signal \n(difference from historical reference period)?",
     )
 
     def __init__(self, *args, **params):
@@ -303,7 +304,7 @@ def __init__(self, *args, **params):
         self.variable = "Air Temperature at 2m"
 
         # Choosing specific warming levels
-        self.warming_levels = ["1.5", "2.0", "3.0", "4.0"]
+        self.warming_levels = ["0.8", "1.2", "1.5", "2.0", "3.0", "4.0"]
         self.months = np.arange(1, 13)
 
         # Location defaults