diff --git a/notebooks/spe11b_task_1.ipynb b/notebooks/spe11b_task_1.ipynb
index fb329c5..61a8518 100644
--- a/notebooks/spe11b_task_1.ipynb
+++ b/notebooks/spe11b_task_1.ipynb
@@ -34,7 +34,7 @@
     "- `rhoG [kg/m3]`\n",
     "- `rhoL [kg/m3]`\n",
     "- `tmCO2 [kg]`\n",
-    "- `temp [°C]`\n",
+    "- `temp [\u00b0C]`\n",
     "\n",
     "The surrogate predicts the state variables forward in time and keeps the coordinates fixed from the template CSV.\n",
     "\n",
@@ -135,7 +135,7 @@
     "\n",
     "# Train on one participant from the first 50 years only.\n",
     "train_participant = participants[0]\n",
-    "export_participant = participants[3]\n",
+    "export_participant = participants[4]\n",
     "train_dataset = SpatialMapTransitionDataset(\n",
     "    data_root,\n",
     "    participants=[train_participant],\n",
@@ -454,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "329316dc",
    "metadata": {},
    "outputs": [],
@@ -479,9 +479,13 @@
     "ar_summaries = []\n",
     "ar_feature_names = [name for name in field_names if name not in ['x [m]', 'z [m]', 'pressure [Pa]']]\n",
     "ar_feature_names = [name for name in ar_feature_names if name in physical_fields]\n",
-    "ar_feature_traces = {name: [] for name in ar_feature_names}\n",
+    "ar_feature_traces = {name: [0.0] for name in ar_feature_names}\n",
     "\n",
     "current_snapshot = export_series.snapshots[0]\n",
+    "initial_output_path = ar_root / export_series.participant / current_snapshot.path.name\n",
+    "save_spatial_map_csv_data(current_snapshot, initial_output_path)\n",
+    "ar_outputs.append(initial_output_path)\n",
+    "ar_summaries.append({'target_time_years': current_snapshot.time_years, 'pressure [Pa]': 0.0})\n",
     "for target_snapshot in export_series.snapshots[1:]:\n",
     "    predicted_data = predict_autoregressive_data(current_snapshot, target_snapshot)\n",
     "    output_path = ar_root / export_series.participant / target_snapshot.path.name\n",
@@ -625,4 +629,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/src/ml4gcs/data/export.py b/src/ml4gcs/data/export.py
index a5f8c79..cbda624 100644
--- a/src/ml4gcs/data/export.py
+++ b/src/ml4gcs/data/export.py
@@ -50,19 +50,22 @@ def save_spatial_map_csv(
 def save_spatial_map_csv_data(
     template: SpatialMapSnapshot,
     output_path: Path | str,
-    data: np.ndarray,
+    data: np.ndarray | None = None,
 ) -> Path:
     """Write a spatial-map CSV using a full row matrix."""
 
     output_path = Path(output_path)
     output_path.parent.mkdir(parents=True, exist_ok=True)
 
-    data = np.asarray(data, dtype=template.data.dtype)
-    if data.shape != template.data.shape:
-        raise ValueError(
-            "Predicted data shape does not match the template shape: "
-            f"{data.shape} vs {template.data.shape}"
-        )
+    if data is None:
+        data = template.data.copy()
+    else:
+        data = np.asarray(data, dtype=template.data.dtype)
+        if data.shape != template.data.shape:
+            raise ValueError(
+                "Predicted data shape does not match the template shape: "
+                f"{data.shape} vs {template.data.shape}"
+            )
 
     with output_path.open("w", encoding="utf-8", newline="") as handle:
         handle.write("# " + ", ".join(template.columns) + "\n")
@@ -109,6 +112,12 @@ def export_next_step_timeline(
     output_paths: list[Path] = []
     snapshots = participant_series.snapshots
 
+    if snapshots:
+        first_snapshot = snapshots[0]
+        output_paths.append(
+            save_spatial_map_csv_data(first_snapshot, output_root / first_snapshot.path.name)
+        )
+
     for input_snapshot, target_snapshot in zip(snapshots[:-1], snapshots[1:], strict=True):
         pressure_grid = predict_pressure(input_snapshot, target_snapshot)
         output_path = output_root / target_snapshot.path.name
@@ -128,6 +137,10 @@ def export_next_step_timeline_data(
     output_paths: list[Path] = []
     snapshots = participant_series.snapshots
 
+    if snapshots:
+        first_snapshot = snapshots[0]
+        output_paths.append(save_spatial_map_csv_data(first_snapshot, output_root / first_snapshot.path.name))
+
     for input_snapshot, target_snapshot in zip(snapshots[:-1], snapshots[1:], strict=True):
         predicted = predict_data(input_snapshot, target_snapshot)
         output_path = output_root / target_snapshot.path.name