Fix land variable performance issue by eagerly loading data

Land variables were taking ~18 minutes each vs ~5 seconds for atmosphere
variables. The issue was dask lazy evaluation - when area scaling arrays
(total_land_area, north_land_area, south_land_area) remained as lazy dask
arrays, the multiplication operation triggered loading all data from disk,
causing the massive delay.

Solution: Eagerly load both area fields and computed data arrays into
memory before performing operations. This ensures all operations work with
numpy arrays instead of lazy dask arrays.

Changes:
- For TOTAL metric variables, call .load() on area fields
  (valid_area_per_gridcell, area, landfrac) after opening dataset
- Call .load() on annual average data_array after computation
- Reduces land variable processing from ~18 minutes to ~5-10 seconds

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <[email protected]>

Author: chengzhuzhang

zppy_interfaces/global_time_series/coupled_global/utils.py

@@ -178,11 +178,23 @@ def process_variable(
         # 2. Load only this variable's data
         dataset = xcdat.open_mfdataset(file_paths, center_times=True)
 
+        # For TOTAL metrics, eagerly load area fields to avoid lazy computation issues
+        if var.metric == Metric.TOTAL:
+            if "valid_area_per_gridcell" in dataset:
+                dataset["valid_area_per_gridcell"].load()
+            if "area" in dataset:
+                dataset["area"].load()
+            if "landfrac" in dataset:
+                dataset["landfrac"].load()
+
         try:
             # 3. Compute annual average
             annual_dataset = dataset.temporal.group_average(var.variable_name, "year")
             data_array = annual_dataset.data_vars[var.variable_name]
 
+            # Eagerly load the result to avoid lazy computation issues
+            data_array.load()
+
             # 4. Apply area scaling if needed
             data_array = apply_scaling(data_array, var.metric, dataset)