diff --git a/src/infrasys/arrow_storage.py b/src/infrasys/arrow_storage.py
index a1dc7ce..4bf2f02 100644
--- a/src/infrasys/arrow_storage.py
+++ b/src/infrasys/arrow_storage.py
@@ -116,7 +116,14 @@ def _get_single_time_series(
             base_ts = pa.ipc.open_file(source).get_record_batch(0)
             logger.trace("Reading time series from {}", fpath)
         index, length = metadata.get_range(start_time=start_time, length=length)
-        data = base_ts[str(metadata.time_series_uuid)][index : index + length]
+        columns = base_ts.column_names
+        if len(columns) != 1:
+            msg = f"Bug: expected a single column: {columns=}"
+            raise Exception(msg)
+        # This should be equal to metadata.time_series_uuid in versions
+        # v0.2.1 or later. Earlier versions used the time series variable name.
+        column = columns[0]
+        data = base_ts[column][index : index + length]
         if metadata.quantity_metadata is not None:
             np_array = metadata.quantity_metadata.quantity_type(
                 data, metadata.quantity_metadata.units
@@ -137,14 +144,17 @@ def get_raw_single_time_series(self, time_series_uuid: UUID) -> NDArray:
         with pa.OSFile(str(fpath), "r") as source:
             base_ts = pa.ipc.open_file(source).get_record_batch(0)
             logger.trace("Reading time series from {}", fpath)
-        return base_ts[str(time_series_uuid)].to_numpy()
-
-    def _convert_to_record_batch(
-        self, time_series_array: NDArray, variable_name: str
-    ) -> pa.RecordBatch:
+        columns = base_ts.column_names
+        if len(columns) != 1:
+            msg = f"Bug: expected a single column: {columns=}"
+            raise Exception(msg)
+        column = columns[0]
+        return base_ts[column].to_numpy()
+
+    def _convert_to_record_batch(self, time_series_array: NDArray, column: str) -> pa.RecordBatch:
         """Create record batch to save array to disk."""
         pa_array = pa.array(time_series_array)
-        schema = pa.schema([pa.field(variable_name, pa_array.type)])
+        schema = pa.schema([pa.field(column, pa_array.type)])
         return pa.record_batch([pa_array], schema=schema)