diff --git a/scripts/add_brownfield.py b/scripts/add_brownfield.py
index 0f2d6aa3..672f9e62 100644
--- a/scripts/add_brownfield.py
+++ b/scripts/add_brownfield.py
@@ -132,22 +132,20 @@ def disable_grid_expansion_if_limit_hit(n):
     minimum and extendable is turned off; the corresponding global
     constraint is then dropped.
     """
-    cols = {"cost": "capital_cost", "volume": "length"}
-    for limit_type in ["cost", "volume"]:
-        glcs = n.global_constraints.query(
-            f"type == 'transmission_expansion_{limit_type}_limit'"
-        )
+    types = {"expansion_cost": "capital_cost", "volume_expansion": "length"}
+    for limit_type in types:
+        glcs = n.global_constraints.query(f"type == 'transmission_{limit_type}_limit'")
 
         for name, glc in glcs.iterrows():
             total_expansion = (
                 (
                     n.lines.query("s_nom_extendable")
-                    .eval(f"s_nom_min * {cols[limit_type]}")
+                    .eval(f"s_nom_min * {types[limit_type]}")
                     .sum()
                 )
                 + (
                     n.links.query("carrier == 'DC' and p_nom_extendable")
-                    .eval(f"p_nom_min * {cols[limit_type]}")
+                    .eval(f"p_nom_min * {types[limit_type]}")
                     .sum()
                 )
             ).sum()
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index fb290130..3f8dd022 100755
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -3634,15 +3634,13 @@ def set_temporal_aggregation(n, resolution, snapshot_weightings):
         logger.info("Use every %s snapshot as representative", sn)
         n.set_snapshots(n.snapshots[::sn])
         n.snapshot_weightings *= sn
+        return n
     else:
         # Otherwise, use the provided snapshots
         snapshot_weightings = pd.read_csv(
             snapshot_weightings, index_col=0, parse_dates=True
         )
 
-        n.set_snapshots(snapshot_weightings.index)
-        n.snapshot_weightings = snapshot_weightings
-
         # Define a series used for aggregation, mapping each hour in
         # n.snapshots to the closest previous timestep in
         # snapshot_weightings.index
@@ -3656,16 +3654,23 @@ def set_temporal_aggregation(n, resolution, snapshot_weightings):
             .map(lambda i: snapshot_weightings.index[i])
         )
 
+        m = n.copy(with_time=False)
+        m.set_snapshots(snapshot_weightings.index)
+        m.snapshot_weightings = snapshot_weightings
+
         # Aggregation all time-varying data.
         for c in n.iterate_components():
+            pnl = getattr(m, c.list_name + "_t")
             for k, df in c.pnl.items():
                 if not df.empty:
                     if c.list_name == "stores" and k == "e_max_pu":
-                        c.pnl[k] = df.groupby(aggregation_map).min()
+                        pnl[k] = df.groupby(aggregation_map).min()
                     elif c.list_name == "stores" and k == "e_min_pu":
-                        c.pnl[k] = df.groupby(aggregation_map).max()
+                        pnl[k] = df.groupby(aggregation_map).max()
                     else:
-                        c.pnl[k] = df.groupby(aggregation_map).mean()
+                        pnl[k] = df.groupby(aggregation_map).mean()
+
+        return m
 
 
 def lossy_bidirectional_links(n, carrier, efficiencies={}):
@@ -3818,7 +3823,7 @@ if __name__ == "__main__":
     if options["allam_cycle"]:
         add_allam(n, costs)
 
-    set_temporal_aggregation(
+    n = set_temporal_aggregation(
         n, snakemake.params.time_resolution, snakemake.input.snapshot_weightings
     )