diff --git a/backend/pyproject.toml b/backend/pyproject.toml
index 55d9698..ec9cbec 100644
--- a/backend/pyproject.toml
+++ b/backend/pyproject.toml
@@ -2,14 +2,14 @@
 name = "ref-backend"
 version = "0.3.0"
 description = "Backend for the Climate Rapid Evaluation Framework"
-requires-python = ">=3.11"
+requires-python = ">=3.12"
 dependencies = [
     "fastapi[standard]<1.0.0,>=0.114.2",
     "pydantic>2.0",
     "psycopg[binary]<4.0.0,>=3.1.13",
     "pydantic-settings<3.0.0,>=2.13.1",
     "sentry-sdk[fastapi]>=2.0.0",
-    "climate-ref[aft-providers,postgres]>=0.13.1,<0.14",
+    "climate-ref[aft-providers,postgres]>=0.13.1",
     "loguru",
     "pyyaml>=6.0",
     "fastapi-sqlalchemy-monitor>=1.1.3",
@@ -30,8 +30,10 @@ dev = [
 ]
 
 [tool.uv.sources]
-# Temporary pin for testing
-# climate-ref = { git = "https://github.com/Climate-REF/climate-ref", subdirectory = "packages/climate-ref", tag="v0.7.0" }
+# Pinned to an unreleased commit on climate-ref main that carries the shared
+# metric-value contract (kind, reference_id, typed presentation attributes).
+# Switch back to a plain PyPI version constraint once this ships in a release.
+climate-ref = { git = "https://github.com/Climate-REF/climate-ref", subdirectory = "packages/climate-ref", rev = "383bd3f81e4af8c40a3f162eb529b5e6c2e05a1f" }
 # climate-ref-example = { git = "https://github.com/Climate-REF/climate-ref", subdirectory = "packages/climate-ref-example", tag="v0.7.0" }
 # Uncomment the following line to use a local version of climate-ref
 #climate-ref = { path = "../../climate-ref/packages/climate-ref", editable = true }
diff --git a/backend/src/ref_backend/core/outliers.py b/backend/src/ref_backend/core/outliers.py
index ae1516c..c07ee09 100644
--- a/backend/src/ref_backend/core/outliers.py
+++ b/backend/src/ref_backend/core/outliers.py
@@ -58,8 +58,14 @@ def calculate_iqr_bounds_by_source_id(
     if "source_id" not in df.columns:
         return None
 
-    # Separate Reference values (exclude from IQR calculation)
-    reference_mask = df["source_id"] == "Reference"
+    # Separate reference values (exclude from IQR calculation) using the
+    # first-class `kind` signal. A missing/None/empty kind is treated as a
+    # model. If the `kind` column is absent entirely (older data), fall back
+    # to treating all rows as models (no reference exclusion).
+    if "kind" in df.columns:
+        reference_mask = df["kind"] == "reference"
+    else:
+        reference_mask = pd.Series(False, index=df.index)
     non_reference_df = df[~reference_mask]
 
     # Group by source_id and calculate mean for each
@@ -138,11 +144,11 @@ def detect_outliers_in_scalar_values(
 
             if iqr_bounds is not None:
                 lower_bound, upper_bound = iqr_bounds
-                # Apply bounds to individual values (Reference values always non-outlier)
+                # Apply bounds to individual values (reference values always non-outlier)
                 source_id_flags = group_values.apply(
                     lambda row: (
                         (row["value"] < lower_bound or row["value"] > upper_bound)
-                        if row["source_id"] != "Reference"
+                        if row.get("kind") != "reference"
                         else False
                     ),
                     axis=1,
@@ -154,6 +160,12 @@ def detect_outliers_in_scalar_values(
             # Fallback to original IQR method if no source_id or insufficient data
             if len(group_values) >= min_n:
                 iqr_flags = flag_outliers_iqr(group_values.value.to_list(), factor=factor)
+                # Reference values are always non-outlier, even on this fallback path.
+                if "kind" in group_values.columns:
+                    iqr_flags = [
+                        False if kind == "reference" else flag
+                        for flag, kind in zip(iqr_flags, group_values["kind"])
+                    ]
             else:
                 iqr_flags = [False] * len(group_values)
             source_id_flags = iqr_flags  # type: ignore
diff --git a/backend/src/ref_backend/models.py b/backend/src/ref_backend/models.py
index 92c6467..0b81e8b 100644
--- a/backend/src/ref_backend/models.py
+++ b/backend/src/ref_backend/models.py
@@ -540,6 +540,12 @@ class SeriesValue(BaseModel):
     attributes: dict[str, Union[str, float]] | None = None
     execution_group_id: int
     execution_id: int
+    kind: Literal["model", "reference"] = "model"
+    reference_id: str | None = None
+    value_units: str | None = None
+    value_long_name: str | None = None
+    index_units: str | None = None
+    calendar: str | None = None
 
 
 class Facet(BaseModel):
@@ -554,6 +560,48 @@ class AnnotatedScalarValue:
     verification_status: Literal["verified", "unverified"] | None = None
 
 
+def _normalize_kind(dimensions: dict[str, str]) -> Literal["model", "reference"]:
+    """
+    Normalise the ``kind`` CV dimension to the model/reference role.
+
+    ``kind`` is absent from ``dimensions`` for model rows (the committed
+    default is omitted at serialisation), so a missing or empty value is
+    treated as ``"model"``.
+    """
+    kind = dimensions.get("kind")
+    return "reference" if kind == "reference" else "model"
+
+
+_PRESENTATION_ATTRIBUTE_FALLBACKS: dict[str, tuple[str, ...]] = {
+    "value_units": ("value_units", "units"),
+    "value_long_name": ("value_long_name", "long_name"),
+    "index_units": ("index_units",),
+    "calendar": ("calendar",),
+}
+
+
+def _normalize_presentation_attributes(
+    attributes: dict[str, Union[str, float]] | None,
+) -> dict[str, str | None]:
+    """
+    Normalise provider-specific presentation attribute keys to the shared names.
+
+    Providers disagree on attribute keys (ESMValTool already uses the target
+    names, ILAMB uses ``units``/``long_name``), so the first present key in
+    each fallback chain wins; a series with no matching key surfaces ``None``.
+    """
+    attributes = attributes or {}
+    normalized: dict[str, str | None] = {}
+    for target, fallback_keys in _PRESENTATION_ATTRIBUTE_FALLBACKS.items():
+        value: str | None = None
+        for key in fallback_keys:
+            if key in attributes and attributes[key] is not None:
+                value = str(attributes[key])
+                break
+        normalized[target] = value
+    return normalized
+
+
 class MetricValueCollection(BaseModel):
     data: Sequence[ScalarValue | SeriesValue]
     count: int
@@ -588,6 +636,7 @@ def build_scalar(
                     execution_id=v.execution_id,
                     is_outlier=item.is_outlier,
                     verification_status=item.verification_status,
+                    kind=_normalize_kind(v.dimensions),
                 )
             )
 
@@ -615,6 +664,7 @@ def build_series(
         all_data: list[ScalarValue | SeriesValue] = []
 
         for series in series_values:
+            presentation = _normalize_presentation_attributes(series.attributes)
             all_data.append(
                 SeriesValue(
                     id=series.id,
@@ -625,6 +675,9 @@ def build_series(
                     index_name=series.index_name,
                     execution_group_id=series.execution.execution_group_id,
                     execution_id=series.execution_id,
+                    kind=_normalize_kind(series.dimensions),
+                    reference_id=series.reference_id,
+                    **presentation,
                 )
             )
 
diff --git a/backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/db/climate_ref.db b/backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/db/climate_ref.db
index 1e5b868..2da7b8e 100644
Binary files a/backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/db/climate_ref.db and b/backend/tests/test-data/tests.integration.test_cmip7_aft/test_solve_cmip7_aft/db/climate_ref.db differ
diff --git a/backend/tests/test_core/test_metric_values.py b/backend/tests/test_core/test_metric_values.py
index 36c8b2f..89894bc 100644
--- a/backend/tests/test_core/test_metric_values.py
+++ b/backend/tests/test_core/test_metric_values.py
@@ -14,7 +14,7 @@
     parse_id_list,
     process_scalar_values,
 )
-from ref_backend.models import AnnotatedScalarValue
+from ref_backend.models import AnnotatedScalarValue, MetricValueCollection, _normalize_kind
 
 
 class TestParseIdList:
@@ -399,3 +399,172 @@ def test_series_with_detection_headers(self):
         assert response.headers["X-REF-Had-Outliers"] == "true"
         assert response.headers["X-REF-Outlier-Count"] == "5"
         assert response.headers["Content-Disposition"] == "attachment; filename=test.csv"
+
+
+class TestBuildScalar:
+    """Test MetricValueCollection.build_scalar sets kind from dimensions."""
+
+    def test_kind_read_from_dimensions(self):
+        """A scalar row with kind="reference" in its dimensions surfaces kind="reference"."""
+        mock_value = Mock(
+            id=1,
+            dimensions={"metric": "rmse", "kind": "reference"},
+            attributes=None,
+            value=1.5,
+            execution_id=2,
+        )
+        mock_value.execution = Mock(execution_group_id=1)
+
+        collection = MetricValueCollection.build_scalar(
+            [AnnotatedScalarValue(value=mock_value)],
+            total_count=1,
+            facets=[],
+        )
+
+        assert collection.data[0].kind == "reference"
+
+    def test_missing_kind_defaults_to_model(self):
+        """A scalar row without kind in its dimensions defaults to kind="model"."""
+        mock_value = Mock(
+            id=1,
+            dimensions={"metric": "rmse"},
+            attributes=None,
+            value=1.5,
+            execution_id=2,
+        )
+        mock_value.execution = Mock(execution_group_id=1)
+
+        collection = MetricValueCollection.build_scalar(
+            [AnnotatedScalarValue(value=mock_value)],
+            total_count=1,
+            facets=[],
+        )
+
+        assert collection.data[0].kind == "model"
+
+    def test_normalize_kind_treats_none_and_empty_as_model(self):
+        """The kind-normalisation helper treats missing, None and empty kind as model."""
+        assert _normalize_kind({"metric": "rmse"}) == "model"
+        assert _normalize_kind({"metric": "rmse", "kind": None}) == "model"
+        assert _normalize_kind({"metric": "rmse", "kind": ""}) == "model"
+        assert _normalize_kind({"metric": "rmse", "kind": "reference"}) == "reference"
+
+
+class TestBuildSeries:
+    """Test MetricValueCollection.build_series surfaces kind, reference_id and presentation attrs."""
+
+    def test_esmvaltool_style_attributes(self):
+        """ESMValTool-style attribute keys already match the target names."""
+        mock_series = Mock(
+            id=1,
+            dimensions={"metric": "temp"},
+            attributes={
+                "value_units": "K",
+                "value_long_name": "Near-Surface Air Temperature",
+                "index_units": "days since 1850-01-01",
+                "calendar": "standard",
+                "index_long_name": "time",
+            },
+            values=[1.0, 2.0],
+            index=[2020, 2021],
+            index_name="time",
+            reference_id=None,
+            execution_id=2,
+        )
+        mock_series.execution = Mock(execution_group_id=1)
+
+        collection = MetricValueCollection.build_series([mock_series], total_count=1, facets=[])
+
+        value = collection.data[0]
+        assert value.value_units == "K"
+        assert value.value_long_name == "Near-Surface Air Temperature"
+        assert value.index_units == "days since 1850-01-01"
+        assert value.calendar == "standard"
+
+    def test_ilamb_style_attributes_fall_back(self):
+        """ILAMB-style keys (units, long_name) fall back onto the target names."""
+        mock_series = Mock(
+            id=1,
+            dimensions={"metric": "temp"},
+            attributes={
+                "units": "percent",
+                "long_name": "Bias",
+                "standard_name": "bias",
+            },
+            values=[1.0, 2.0],
+            index=[2020, 2021],
+            index_name="time",
+            reference_id=None,
+            execution_id=2,
+        )
+        mock_series.execution = Mock(execution_group_id=1)
+
+        collection = MetricValueCollection.build_series([mock_series], total_count=1, facets=[])
+
+        value = collection.data[0]
+        assert value.value_units == "percent"
+        assert value.value_long_name == "Bias"
+        assert value.index_units is None
+        assert value.calendar is None
+
+    def test_missing_attributes_are_none(self):
+        """A series with attributes=None surfaces None presentation fields, not an error."""
+        mock_series = Mock(
+            id=1,
+            dimensions={"metric": "temp"},
+            attributes=None,
+            values=[1.0, 2.0],
+            index=[2020, 2021],
+            index_name="time",
+            reference_id=None,
+            execution_id=2,
+        )
+        mock_series.execution = Mock(execution_group_id=1)
+
+        collection = MetricValueCollection.build_series([mock_series], total_count=1, facets=[])
+
+        value = collection.data[0]
+        assert value.value_units is None
+        assert value.value_long_name is None
+        assert value.index_units is None
+        assert value.calendar is None
+
+    def test_reference_series_sets_kind_and_reference_id(self):
+        """A reference series surfaces kind="reference" and its reference_id."""
+        mock_series = Mock(
+            id=1,
+            dimensions={"metric": "temp", "kind": "reference"},
+            attributes=None,
+            values=[1.0, 2.0],
+            index=[2020, 2021],
+            index_name="time",
+            reference_id="abc123",
+            execution_id=2,
+        )
+        mock_series.execution = Mock(execution_group_id=1)
+
+        collection = MetricValueCollection.build_series([mock_series], total_count=1, facets=[])
+
+        value = collection.data[0]
+        assert value.kind == "reference"
+        assert value.reference_id == "abc123"
+
+    def test_model_series_defaults_kind_and_no_reference_id(self):
+        """A model series (kind absent from dimensions) defaults to kind="model" with no reference_id."""
+        mock_series = Mock(
+            id=1,
+            dimensions={"metric": "temp"},
+            attributes=None,
+            values=[1.0, 2.0],
+            index=[2020, 2021],
+            index_name="time",
+            reference_id=None,
+            execution_id=2,
+        )
+        mock_series.execution = Mock(execution_group_id=1)
+
+        collection = MetricValueCollection.build_series([mock_series], total_count=1, facets=[])
+
+        value = collection.data[0]
+        assert value.kind == "model"
+        assert value.reference_id is None
diff --git a/backend/tests/test_core/test_outliers.py b/backend/tests/test_core/test_outliers.py
index 49a2128..16bf7d8 100644
--- a/backend/tests/test_core/test_outliers.py
+++ b/backend/tests/test_core/test_outliers.py
@@ -1,6 +1,12 @@
 from unittest.mock import Mock
 
-from ref_backend.core.outliers import detect_outliers_in_scalar_values, flag_outliers_iqr
+import pandas as pd
+
+from ref_backend.core.outliers import (
+    calculate_iqr_bounds_by_source_id,
+    detect_outliers_in_scalar_values,
+    flag_outliers_iqr,
+)
 
 
 class TestFlagOutliersIQR:
@@ -66,6 +72,60 @@ def test_normal_outlier_detection(self):
         assert result == expected
 
 
+class TestCalculateIqrBoundsBySourceId:
+    """Test the kind-aware IQR bound calculation."""
+
+    def test_excludes_rows_tagged_kind_reference(self):
+        """Rows with kind == 'reference' are excluded from the IQR calculation,
+        regardless of their source_id."""
+        df = pd.DataFrame(
+            [
+                {"source_id": "model-a", "kind": "model", "value": 1.0},
+                {"source_id": "model-b", "kind": "model", "value": 2.0},
+                {"source_id": "model-c", "kind": "model", "value": 3.0},
+                {"source_id": "model-d", "kind": "model", "value": 4.0},
+                # A reference row with an extreme value that must not skew the bounds.
+                {"source_id": "model-e", "kind": "reference", "value": 1000.0},
+            ]
+        )
+        bounds_with_reference = calculate_iqr_bounds_by_source_id(df, factor=3.0, min_n=4)
+
+        df_without_reference = df[df["kind"] != "reference"]
+        bounds_without_reference = calculate_iqr_bounds_by_source_id(
+            df_without_reference, factor=3.0, min_n=4
+        )
+
+        assert bounds_with_reference == bounds_without_reference
+
+    def test_missing_kind_column_falls_back_to_no_exclusion(self):
+        """When the kind column is absent entirely (older data), no rows are
+        treated as reference — fall back to treating all rows as models."""
+        df = pd.DataFrame(
+            [
+                {"source_id": "model-a", "value": 1.0},
+                {"source_id": "model-b", "value": 2.0},
+                {"source_id": "model-c", "value": 3.0},
+                {"source_id": "model-d", "value": 4.0},
+            ]
+        )
+        bounds = calculate_iqr_bounds_by_source_id(df, factor=3.0, min_n=4)
+        assert bounds is not None
+
+    def test_none_or_empty_kind_treated_as_model(self):
+        """A None or empty kind is treated as a model, not a reference."""
+        df = pd.DataFrame(
+            [
+                {"source_id": "model-a", "kind": None, "value": 1.0},
+                {"source_id": "model-b", "kind": "", "value": 2.0},
+                {"source_id": "model-c", "kind": "model", "value": 3.0},
+                {"source_id": "model-d", "kind": "model", "value": 4.0},
+            ]
+        )
+        bounds = calculate_iqr_bounds_by_source_id(df, factor=3.0, min_n=4)
+        # All 4 source_ids should participate (none excluded as reference).
+        assert bounds is not None
+
+
 class TestDetectOutliersInScalarValues:
     """Test the detect_outliers_in_scalar_values function with grouping and auto-flags."""
 
@@ -82,3 +142,184 @@ def test_auto_flag_non_finite_values(self):
 
         assert flags == [False, True, True]
         assert outlier_count == 2
+
+    def test_reference_rows_excluded_and_always_non_outlier(self):
+        """Rows tagged kind='reference' are excluded from IQR-bound computation
+        and are always flagged as non-outlier, regardless of source_id."""
+        mock_values = [
+            Mock(
+                dimensions={
+                    "metric": "rmse",
+                    "statistic": "mean",
+                    "source_id": f"model-{i}",
+                    "kind": "model",
+                },
+                value=float(i),
+                id=i,
+            )
+            for i in range(4)
+        ] + [
+            Mock(
+                dimensions={
+                    "metric": "rmse",
+                    "statistic": "mean",
+                    "source_id": "obs-source",
+                    "kind": "reference",
+                },
+                value=1000.0,
+                id=100,
+            )
+        ]
+
+        annotated, _ = detect_outliers_in_scalar_values(mock_values, min_n=4)
+        by_id = {item.value.id: item for item in annotated}
+
+        assert by_id[100].is_outlier is False
+        assert by_id[100].verification_status == "verified"
+
+    def test_reference_row_non_outlier_on_fallback_path_without_source_id(self):
+        """Rows tagged kind='reference' stay non-outlier even on the fallback
+        IQR path taken when no source_id dimension is present. Without the
+        kind override on that path, the extreme reference value would be
+        flagged an outlier."""
+        mock_values = [
+            Mock(
+                dimensions={"metric": "rmse", "statistic": "mean", "kind": "model"},
+                value=float(i),
+                id=i,
+            )
+            for i in range(4)
+        ] + [
+            Mock(
+                dimensions={"metric": "rmse", "statistic": "mean", "kind": "reference"},
+                value=1000.0,
+                id=100,
+            )
+        ]
+
+        annotated, _ = detect_outliers_in_scalar_values(mock_values, min_n=4)
+        by_id = {item.value.id: item for item in annotated}
+
+        assert by_id[100].is_outlier is False
+        assert by_id[100].verification_status == "verified"
+
+    def test_source_id_reference_sentinel_with_kind_model_is_treated_as_model(self):
+        """Regression: a row with source_id == 'Reference' but kind == 'model'
+        is now treated as a model — it participates in IQR and can be flagged
+        an outlier. This proves the source_id sentinel is gone."""
+        mock_values = [
+            Mock(
+                dimensions={"metric": "rmse", "statistic": "mean", "source_id": "model-a", "kind": "model"},
+                value=1.0,
+                id=0,
+            ),
+            Mock(
+                dimensions={"metric": "rmse", "statistic": "mean", "source_id": "model-b", "kind": "model"},
+                value=2.0,
+                id=1,
+            ),
+            Mock(
+                dimensions={"metric": "rmse", "statistic": "mean", "source_id": "model-c", "kind": "model"},
+                value=3.0,
+                id=2,
+            ),
+            Mock(
+                dimensions={"metric": "rmse", "statistic": "mean", "source_id": "model-d", "kind": "model"},
+                value=4.0,
+                id=3,
+            ),
+            # Sentinel source_id, but kind says it's actually a model — should
+            # be an extreme outlier that participates in (and blows up) the IQR bounds.
+            Mock(
+                dimensions={"metric": "rmse", "statistic": "mean", "source_id": "Reference", "kind": "model"},
+                value=1000.0,
+                id=4,
+            ),
+        ]
+
+        annotated, outlier_count = detect_outliers_in_scalar_values(mock_values, factor=3.0, min_n=4)
+        by_id = {item.value.id: item for item in annotated}
+
+        assert by_id[4].is_outlier is True
+        assert by_id[4].verification_status == "unverified"
+        assert outlier_count == 1
+
+    def test_arbitrary_source_id_with_kind_reference_is_treated_as_reference(self):
+        """A row with source_id == 'ACCESS-CM2' but kind == 'reference' is
+        treated as a reference: excluded from IQR computation and always
+        flagged non-outlier."""
+        mock_values = [
+            Mock(
+                dimensions={"metric": "rmse", "statistic": "mean", "source_id": "model-a", "kind": "model"},
+                value=1.0,
+                id=0,
+            ),
+            Mock(
+                dimensions={"metric": "rmse", "statistic": "mean", "source_id": "model-b", "kind": "model"},
+                value=2.0,
+                id=1,
+            ),
+            Mock(
+                dimensions={"metric": "rmse", "statistic": "mean", "source_id": "model-c", "kind": "model"},
+                value=2.0,
+                id=2,
+            ),
+            Mock(
+                dimensions={"metric": "rmse", "statistic": "mean", "source_id": "model-d", "kind": "model"},
+                value=2.0,
+                id=3,
+            ),
+            # Real model source_id, but kind says it's a reference — should be
+            # excluded from IQR and always non-outlier despite the extreme value.
+            Mock(
+                dimensions={
+                    "metric": "rmse",
+                    "statistic": "mean",
+                    "source_id": "ACCESS-CM2",
+                    "kind": "reference",
+                },
+                value=1000.0,
+                id=4,
+            ),
+        ]
+
+        annotated, outlier_count = detect_outliers_in_scalar_values(mock_values, factor=3.0, min_n=4)
+        by_id = {item.value.id: item for item in annotated}
+
+        assert by_id[4].is_outlier is False
+        assert by_id[4].verification_status == "verified"
+        assert outlier_count == 0
+
+    def test_plain_model_rows_outlier_behaviour_unchanged(self):
+        """Existing outlier behaviour for plain model rows (kind='model') still
+        holds: an extreme model value is flagged an outlier."""
+        mock_values = [
+            Mock(
+                dimensions={
+                    "metric": "rmse",
+                    "statistic": "mean",
+                    "source_id": f"model-{i}",
+                    "kind": "model",
+                },
+                value=2.0,
+                id=i,
+            )
+            for i in range(4)
+        ] + [
+            Mock(
+                dimensions={
+                    "metric": "rmse",
+                    "statistic": "mean",
+                    "source_id": "model-outlier",
+                    "kind": "model",
+                },
+                value=1000.0,
+                id=100,
+            )
+        ]
+
+        annotated, outlier_count = detect_outliers_in_scalar_values(mock_values, factor=3.0, min_n=4)
+        by_id = {item.value.id: item for item in annotated}
+
+        assert by_id[100].is_outlier is True
+        assert outlier_count == 1
diff --git a/backend/uv.lock b/backend/uv.lock
index 93dad29..3b67c93 100644
--- a/backend/uv.lock
+++ b/backend/uv.lock
@@ -1,15 +1,13 @@
 version = 1
 revision = 3
-requires-python = ">=3.11"
+requires-python = ">=3.12"
 resolution-markers = [
     "python_full_version >= '3.14' and platform_machine == 'ARM64' and sys_platform == 'win32'",
     "python_full_version == '3.13.*' and platform_machine == 'ARM64' and sys_platform == 'win32'",
-    "python_full_version == '3.12.*' and platform_machine == 'ARM64' and sys_platform == 'win32'",
-    "python_full_version < '3.12' and platform_machine == 'ARM64' and sys_platform == 'win32'",
+    "python_full_version < '3.13' and platform_machine == 'ARM64' and sys_platform == 'win32'",
     "(python_full_version >= '3.14' and platform_machine != 'ARM64') or (python_full_version >= '3.14' and sys_platform != 'win32')",
     "(python_full_version == '3.13.*' and platform_machine != 'ARM64') or (python_full_version == '3.13.*' and sys_platform != 'win32')",
-    "(python_full_version == '3.12.*' and platform_machine != 'ARM64') or (python_full_version == '3.12.*' and sys_platform != 'win32')",
-    "(python_full_version < '3.12' and platform_machine != 'ARM64') or (python_full_version < '3.12' and sys_platform != 'win32')",
+    "(python_full_version < '3.13' and platform_machine != 'ARM64') or (python_full_version < '3.13' and sys_platform != 'win32')",
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-    { url = "https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e", size = 10276, upload-time = "2025-06-08T17:06:38.034Z" },
-]
diff --git a/changelog/32.breaking.md b/changelog/32.breaking.md
new file mode 100644
index 0000000..b79e222
--- /dev/null
+++ b/changelog/32.breaking.md
@@ -0,0 +1 @@
+The backend now requires Python 3.12 or newer, following the climate-ref dependency update that powers the shared metric-value contract.
diff --git a/changelog/32.feature.md b/changelog/32.feature.md
new file mode 100644
index 0000000..75c1a21
--- /dev/null
+++ b/changelog/32.feature.md
@@ -0,0 +1 @@
+Series charts now distinguish model and reference data using the shared metric-value contract's role field instead of a dataset naming convention, deduplicate repeated reference series, and label axes with each series' own units. Series that cover different time or index ranges are now aligned by index value rather than by position, so they no longer smear together.
diff --git a/frontend/src/client/schemas.gen.ts b/frontend/src/client/schemas.gen.ts
index b8b73ec..61fa786 100644
--- a/frontend/src/client/schemas.gen.ts
+++ b/frontend/src/client/schemas.gen.ts
@@ -1799,6 +1799,15 @@ export const ScalarValueSchema = {
             type: 'object',
             title: 'Dimensions'
         },
+        kind: {
+            type: 'string',
+            enum: [
+                'model',
+                'reference'
+            ],
+            title: 'Kind',
+            default: 'model'
+        },
         value: {
             anyOf: [
                 {
@@ -1972,6 +1981,70 @@ export const SeriesValueSchema = {
         execution_id: {
             type: 'integer',
             title: 'Execution Id'
+        },
+        kind: {
+            type: 'string',
+            enum: [
+                'model',
+                'reference'
+            ],
+            title: 'Kind',
+            default: 'model'
+        },
+        reference_id: {
+            anyOf: [
+                {
+                    type: 'string'
+                },
+                {
+                    type: 'null'
+                }
+            ],
+            title: 'Reference Id'
+        },
+        value_units: {
+            anyOf: [
+                {
+                    type: 'string'
+                },
+                {
+                    type: 'null'
+                }
+            ],
+            title: 'Value Units'
+        },
+        value_long_name: {
+            anyOf: [
+                {
+                    type: 'string'
+                },
+                {
+                    type: 'null'
+                }
+            ],
+            title: 'Value Long Name'
+        },
+        index_units: {
+            anyOf: [
+                {
+                    type: 'string'
+                },
+                {
+                    type: 'null'
+                }
+            ],
+            title: 'Index Units'
+        },
+        calendar: {
+            anyOf: [
+                {
+                    type: 'string'
+                },
+                {
+                    type: 'null'
+                }
+            ],
+            title: 'Calendar'
         }
     },
     type: 'object',
diff --git a/frontend/src/client/types.gen.ts b/frontend/src/client/types.gen.ts
index f456d0b..aad3cd9 100644
--- a/frontend/src/client/types.gen.ts
+++ b/frontend/src/client/types.gen.ts
@@ -973,6 +973,10 @@ export type ScalarValue = {
     dimensions: {
         [key: string]: string;
     };
+    /**
+     * Kind
+     */
+    kind?: 'model' | 'reference';
     /**
      * Value
      */
@@ -1049,6 +1053,30 @@ export type SeriesValue = {
      * Execution Id
      */
     execution_id: number;
+    /**
+     * Kind
+     */
+    kind?: 'model' | 'reference';
+    /**
+     * Reference Id
+     */
+    reference_id?: string | null;
+    /**
+     * Value Units
+     */
+    value_units?: string | null;
+    /**
+     * Value Long Name
+     */
+    value_long_name?: string | null;
+    /**
+     * Index Units
+     */
+    index_units?: string | null;
+    /**
+     * Calendar
+     */
+    calendar?: string | null;
 };
 
 /**
diff --git a/frontend/src/components/execution/values/series/seriesCanvas.tsx b/frontend/src/components/execution/values/series/seriesCanvas.tsx
index 531f548..bbd965e 100644
--- a/frontend/src/components/execution/values/series/seriesCanvas.tsx
+++ b/frontend/src/components/execution/values/series/seriesCanvas.tsx
@@ -29,6 +29,7 @@ interface SeriesCanvasProps {
   isTimeAxis: boolean;
   metricName?: string;
   units?: string;
+  indexUnits?: string;
   onMouseMove: (e: React.MouseEvent<HTMLCanvasElement>) => void;
   onMouseLeave: () => void;
   onClick: (e: React.MouseEvent<HTMLCanvasElement>) => void;
@@ -52,6 +53,7 @@ export const SeriesCanvas = memo(function SeriesCanvas({
   isTimeAxis,
   metricName,
   units,
+  indexUnits,
   onMouseMove,
   onMouseLeave,
   onClick,
@@ -193,6 +195,7 @@ export const SeriesCanvas = memo(function SeriesCanvas({
       isTimeAxis,
       metricName,
       units,
+      indexUnits,
     );
   }, [
     chartData,
@@ -212,6 +215,7 @@ export const SeriesCanvas = memo(function SeriesCanvas({
     isTimeAxis,
     metricName,
     units,
+    indexUnits,
   ]);
 
   // Redraw on data/scale changes
@@ -342,6 +346,7 @@ function drawAxes(
   isTimeAxis: boolean,
   metricName?: string,
   units?: string,
+  indexUnits?: string,
 ) {
   ctx.save();
 
@@ -410,12 +415,14 @@ function drawAxes(
     }
   }
 
-  // X axis label
+  // X axis label. Units don't apply to time axes (already rendered as dates).
   ctx.textAlign = "center";
   ctx.textBaseline = "top";
   ctx.font = "14px system-ui, sans-serif";
+  const xLabel =
+    !isTimeAxis && indexUnits ? `${indexName} (${indexUnits})` : indexName;
   ctx.fillText(
-    indexName,
+    xLabel,
     margins.left + innerWidth / 2,
     margins.top + innerHeight + 30,
   );
diff --git a/frontend/src/components/execution/values/series/seriesVisualization.tsx b/frontend/src/components/execution/values/series/seriesVisualization.tsx
index 954f056..78d019f 100644
--- a/frontend/src/components/execution/values/series/seriesVisualization.tsx
+++ b/frontend/src/components/execution/values/series/seriesVisualization.tsx
@@ -67,11 +67,23 @@ export function SeriesVisualization({
   }, []);
 
   // Create chart data and metadata (stable unless data/props change)
-  const { chartData, seriesMetadata, indexName, isTimeAxis } = useMemo(
+  const {
+    chartData,
+    seriesMetadata,
+    indexName,
+    isTimeAxis,
+    valueUnits,
+    indexUnits,
+  } = useMemo(
     () => createChartData(seriesValues, referenceSeriesValues, labelTemplate),
     [seriesValues, referenceSeriesValues, labelTemplate],
   );
 
+  // Prefer per-series value_units for the Y-axis/tooltip unit label; fall
+  // back to the card-config `units` prop when no series carries value_units
+  // (e.g. ILAMB series, which never set it).
+  const effectiveUnits = valueUnits ?? units;
+
   // Available dimension keys for grouping
   const availableDimensions = useMemo(
     () => getDimensionKeys(seriesMetadata),
@@ -370,7 +382,8 @@ export function SeriesVisualization({
             isDark={isDark}
             isTimeAxis={isTimeAxis}
             metricName={metricName}
-            units={units}
+            units={effectiveUnits}
+            indexUnits={indexUnits}
             onMouseMove={handleCanvasMouseMove}
             onMouseLeave={handleCanvasMouseLeave}
             onClick={handleCanvasClick}
@@ -384,7 +397,7 @@ export function SeriesVisualization({
             containerWidth={containerWidth}
             indexName={indexName}
             isTimeAxis={isTimeAxis}
-            units={units}
+            units={effectiveUnits}
           />
         </div>
       </div>
diff --git a/frontend/src/components/execution/values/series/utils.test.ts b/frontend/src/components/execution/values/series/utils.test.ts
index 7fca23a..6c1ca64 100644
--- a/frontend/src/components/execution/values/series/utils.test.ts
+++ b/frontend/src/components/execution/values/series/utils.test.ts
@@ -20,6 +20,7 @@ const mockReferenceSeriesValue: SeriesValue = {
   id: 2,
   execution_group_id: 100,
   execution_id: 201,
+  kind: "reference",
   dimensions: { source_id: "Reference", experiment_id: "exp1", metric: "rmse" },
   values: [1.5, 2.5, 3.5],
   index: [2020, 2021, 2022],
@@ -132,11 +133,12 @@ describe("createChartData", () => {
     expect(result.seriesMetadata[1].color).toBe("#000000");
   });
 
-  it("deduplicates reference series with the same label", () => {
+  it("deduplicates reference series with the same label when reference_id is absent", () => {
     const refSeries2: SeriesValue = {
       id: 10,
       execution_group_id: 101,
       execution_id: 210,
+      kind: "reference",
       dimensions: {
         source_id: "Reference",
         experiment_id: "exp1",
@@ -161,6 +163,7 @@ describe("createChartData", () => {
       id: 11,
       execution_group_id: 101,
       execution_id: 211,
+      kind: "reference",
       dimensions: {
         source_id: "Reference",
         experiment_id: "exp1",
@@ -183,6 +186,112 @@ describe("createChartData", () => {
     );
   });
 
+  it("dedups two reference series with the same reference_id to one", () => {
+    const refA: SeriesValue = {
+      ...mockReferenceSeriesValue,
+      id: 20,
+      execution_id: 220,
+      reference_id: "obs-hash-1",
+      dimensions: {
+        source_id: "Reference",
+        experiment_id: "exp1",
+        metric: "a",
+      },
+    };
+    const refB: SeriesValue = {
+      ...mockReferenceSeriesValue,
+      id: 21,
+      execution_id: 221,
+      reference_id: "obs-hash-1",
+      dimensions: {
+        source_id: "Reference",
+        experiment_id: "exp2",
+        metric: "b",
+      },
+    };
+    const result = createChartData([mockSeriesValue], [refA, refB]);
+    // Different labels (different dimensions), but same reference_id -> deduped to one.
+    expect(result.seriesMetadata).toHaveLength(2);
+    expect(result.seriesMetadata[1].isReference).toBe(true);
+  });
+
+  it("keeps two reference series with different reference_id", () => {
+    const refA: SeriesValue = {
+      ...mockReferenceSeriesValue,
+      id: 22,
+      execution_id: 222,
+      reference_id: "obs-hash-1",
+    };
+    const refB: SeriesValue = {
+      ...mockReferenceSeriesValue,
+      id: 23,
+      execution_id: 223,
+      reference_id: "obs-hash-2",
+    };
+    const result = createChartData([mockSeriesValue], [refA, refB]);
+    expect(result.seriesMetadata).toHaveLength(3);
+    expect(result.seriesMetadata[1].isReference).toBe(true);
+    expect(result.seriesMetadata[2].isReference).toBe(true);
+  });
+
+  it("sets isReference from kind, not array position", () => {
+    const result = createChartData(
+      [mockSeriesValue],
+      [mockReferenceSeriesValue],
+    );
+    expect(result.seriesMetadata[0].isReference).toBe(false);
+    expect(result.seriesMetadata[1].isReference).toBe(true);
+  });
+
+  it("treats a series with kind reference but source_id !== Reference as a reference", () => {
+    const refWithOtherSourceId: SeriesValue = {
+      id: 30,
+      execution_group_id: 100,
+      execution_id: 230,
+      kind: "reference",
+      dimensions: {
+        source_id: "SomeObsDataset",
+        experiment_id: "exp1",
+        metric: "rmse",
+      },
+      values: [1.5, 2.5, 3.5],
+      index: [2020, 2021, 2022],
+      index_name: "year",
+    };
+    const result = createChartData([mockSeriesValue], [refWithOtherSourceId]);
+    expect(result.seriesMetadata[1].isReference).toBe(true);
+  });
+
+  it("treats a series with source_id === Reference but kind model (or absent) as a model, proving the sentinel is gone", () => {
+    const modelWithReferenceSourceId: SeriesValue = {
+      id: 31,
+      execution_group_id: 100,
+      execution_id: 231,
+      kind: "model",
+      dimensions: {
+        source_id: "Reference",
+        experiment_id: "exp1",
+        metric: "rmse",
+      },
+      values: [1.0, 2.0, 3.0],
+      index: [2020, 2021, 2022],
+      index_name: "year",
+    };
+    const modelWithReferenceSourceIdNoKind: SeriesValue = {
+      ...modelWithReferenceSourceId,
+      id: 32,
+      execution_id: 232,
+      kind: undefined,
+    };
+    // Passed in the "regular" bucket, as seriesChartContent.tsx would do based on kind.
+    const result = createChartData(
+      [modelWithReferenceSourceId, modelWithReferenceSourceIdNoKind],
+      [],
+    );
+    expect(result.seriesMetadata[0].isReference).toBe(false);
+    expect(result.seriesMetadata[1].isReference).toBe(false);
+  });
+
   it("uses index name from first series", () => {
     const customSeries: SeriesValue = {
       ...mockSeriesValue,
@@ -318,6 +427,269 @@ describe("createChartData", () => {
   });
 });
 
+describe("createChartData index alignment", () => {
+  it("aligns two series sharing one identical index the same as before (no regression)", () => {
+    const seriesA: SeriesValue = {
+      ...mockSeriesValue,
+      values: [1.0, 2.0, 3.0, 4.0],
+      index: [2020, 2021, 2022, 2023],
+    };
+    const seriesB: SeriesValue = {
+      id: 40,
+      execution_group_id: 100,
+      execution_id: 240,
+      dimensions: {
+        source_id: "ModelB",
+        experiment_id: "exp1",
+        metric: "rmse",
+      },
+      values: [10.0, 20.0, 30.0, 40.0],
+      index: [2020, 2021, 2022, 2023],
+      index_name: "year",
+    };
+    const result = createChartData([seriesA, seriesB], []);
+    expect(result.chartData).toHaveLength(4);
+    expect(result.chartData[0]).toMatchObject({
+      year: 2020,
+      series_0: 1.0,
+      series_1: 10.0,
+    });
+    expect(result.chartData[1]).toMatchObject({
+      year: 2021,
+      series_0: 2.0,
+      series_1: 20.0,
+    });
+    expect(result.chartData[3]).toMatchObject({
+      year: 2023,
+      series_0: 4.0,
+      series_1: 40.0,
+    });
+  });
+
+  it("aligns two series with different index arrays by index value, not position", () => {
+    const seriesA: SeriesValue = {
+      id: 41,
+      execution_group_id: 100,
+      execution_id: 241,
+      dimensions: {
+        source_id: "ModelA",
+        experiment_id: "exp1",
+        metric: "rmse",
+      },
+      values: [1.0, 2.0, 3.0],
+      index: [2020, 2021, 2022],
+      index_name: "year",
+    };
+    const seriesB: SeriesValue = {
+      id: 42,
+      execution_group_id: 100,
+      execution_id: 242,
+      dimensions: {
+        source_id: "ModelB",
+        experiment_id: "exp1",
+        metric: "rmse",
+      },
+      values: [200.0, 300.0, 400.0],
+      // Overlaps at 2021 and 2022, but starts later and ends later than seriesA.
+      index: [2021, 2022, 2023],
+      index_name: "year",
+    };
+    const result = createChartData([seriesA, seriesB], []);
+    // Union of index values: 2020, 2021, 2022, 2023
+    expect(result.chartData).toHaveLength(4);
+
+    const rowByYear = new Map(
+      result.chartData.map((row) => [row.year as number, row]),
+    );
+
+    // seriesA only contributes at 2020-2022; seriesB only at 2021-2023.
+    expect(rowByYear.get(2020)).toMatchObject({ series_0: 1.0 });
+    expect(rowByYear.get(2020)?.series_1 ?? null).toBeNull();
+
+    expect(rowByYear.get(2021)).toMatchObject({
+      series_0: 2.0,
+      series_1: 200.0,
+    });
+    expect(rowByYear.get(2022)).toMatchObject({
+      series_0: 3.0,
+      series_1: 300.0,
+    });
+
+    expect(rowByYear.get(2023)?.series_0 ?? null).toBeNull();
+    expect(rowByYear.get(2023)).toMatchObject({ series_1: 400.0 });
+  });
+
+  it("keeps index-less series visible by keying rows positionally", () => {
+    const seriesNoIndex: SeriesValue = {
+      id: 43,
+      execution_group_id: 100,
+      execution_id: 243,
+      dimensions: { source_id: "ModelA", metric: "rmse" },
+      values: [5.0, 6.0, 7.0],
+      index: null,
+      index_name: null,
+    };
+    const result = createChartData([seriesNoIndex], []);
+    // Without any index array, rows fall back to positional keys 0, 1, 2
+    // rather than collapsing to an empty chart.
+    expect(result.chartData).toHaveLength(3);
+    expect(result.chartData[0]).toMatchObject({ index: 0, series_0: 5.0 });
+    expect(result.chartData[2]).toMatchObject({ index: 2, series_0: 7.0 });
+  });
+
+  it("orders rows by ascending index value when series indices interleave", () => {
+    const seriesA: SeriesValue = {
+      id: 44,
+      execution_group_id: 100,
+      execution_id: 244,
+      dimensions: { source_id: "ModelA", metric: "rmse" },
+      values: [1.0, 3.0, 5.0],
+      index: [1, 3, 5],
+      index_name: "step",
+    };
+    const seriesB: SeriesValue = {
+      id: 45,
+      execution_group_id: 100,
+      execution_id: 245,
+      dimensions: { source_id: "ModelB", metric: "rmse" },
+      values: [2.0, 4.0, 6.0],
+      index: [2, 4, 6],
+      index_name: "step",
+    };
+    const result = createChartData([seriesA, seriesB], []);
+    // Fully interleaved indices must still produce ascending x-values so the
+    // line renders left-to-right instead of zigzagging.
+    const xs = result.chartData.map((row) => row.step as number);
+    expect(xs).toEqual([1, 2, 3, 4, 5, 6]);
+    const rowByStep = new Map(
+      result.chartData.map((row) => [row.step as number, row]),
+    );
+    expect(rowByStep.get(1)).toMatchObject({ series_0: 1.0 });
+    expect(rowByStep.get(2)).toMatchObject({ series_1: 2.0 });
+    expect(rowByStep.get(1)?.series_1 ?? null).toBeNull();
+  });
+
+  it("still renders an index-less series when mixed with an indexed series", () => {
+    const indexed: SeriesValue = {
+      id: 46,
+      execution_group_id: 100,
+      execution_id: 246,
+      dimensions: { source_id: "ModelA", metric: "rmse" },
+      values: [1.0, 2.0, 3.0],
+      index: [2020, 2021, 2022],
+      index_name: "year",
+    };
+    const indexLess: SeriesValue = {
+      id: 47,
+      execution_group_id: 100,
+      execution_id: 247,
+      dimensions: { source_id: "ModelB", metric: "rmse" },
+      values: [10.0, 20.0, 30.0],
+      index: null,
+      index_name: null,
+    };
+    const result = createChartData([indexed, indexLess], []);
+    // The indexed series drives the rows; the index-less series falls back to
+    // the row ordinal so it still renders instead of disappearing.
+    expect(result.chartData).toHaveLength(3);
+    expect(result.chartData[0]).toMatchObject({
+      series_0: 1.0,
+      series_1: 10.0,
+    });
+    expect(result.chartData[2]).toMatchObject({
+      series_0: 3.0,
+      series_1: 30.0,
+    });
+  });
+});
+
+describe("createChartData axis units and calendar", () => {
+  it("derives the Y-axis unit from a series' value_units when present", () => {
+    const seriesWithUnits: SeriesValue = {
+      ...mockSeriesValue,
+      value_units: "kg m-2 s-1",
+    };
+    const result = createChartData([seriesWithUnits], []);
+    expect(result.valueUnits).toBe("kg m-2 s-1");
+  });
+
+  it("falls back to undefined valueUnits when no series carries value_units (caller falls back to the units prop)", () => {
+    const result = createChartData([mockSeriesValue], []);
+    expect(result.valueUnits).toBeUndefined();
+  });
+
+  it("uses the first series with value_units when several series are present", () => {
+    const seriesNoUnits: SeriesValue = { ...mockSeriesValue };
+    const seriesWithUnits: SeriesValue = {
+      ...mockSeriesValue,
+      id: 50,
+      execution_id: 250,
+      value_units: "percent",
+    };
+    const result = createChartData([seriesNoUnits, seriesWithUnits], []);
+    expect(result.valueUnits).toBe("percent");
+  });
+
+  it("derives indexUnits from a series' index_units when present", () => {
+    const seriesWithIndexUnits: SeriesValue = {
+      ...mockSeriesValue,
+      index_units: "days since 1850-01-01",
+    };
+    const result = createChartData([seriesWithIndexUnits], []);
+    expect(result.indexUnits).toBe("days since 1850-01-01");
+  });
+
+  it("handles a series with calendar present without error (ESMValTool case)", () => {
+    const seriesWithCalendar: SeriesValue = {
+      ...mockSeriesValue,
+      index: [
+        "2020-01-15T00:00:00",
+        "2020-02-15T00:00:00",
+        "2020-03-15T00:00:00",
+        "2020-04-15T00:00:00",
+      ],
+      calendar: "standard",
+    };
+    expect(() => createChartData([seriesWithCalendar], [])).not.toThrow();
+    const result = createChartData([seriesWithCalendar], []);
+    expect(result.isTimeAxis).toBe(true);
+    expect(result.calendar).toBe("standard");
+  });
+
+  it("still detects ISO-string time axis when calendar is absent (ILAMB case)", () => {
+    const ilambSeries: SeriesValue = {
+      ...mockSeriesValue,
+      index: [
+        "2020-01-15T00:00:00",
+        "2020-02-15T00:00:00",
+        "2020-03-15T00:00:00",
+        "2020-04-15T00:00:00",
+      ],
+    };
+    const result = createChartData([ilambSeries], []);
+    expect(result.isTimeAxis).toBe(true);
+    expect(result.calendar).toBeUndefined();
+  });
+
+  it("surfaces a non-standard calendar without building a full calendar engine", () => {
+    const nonStandardCalendarSeries: SeriesValue = {
+      ...mockSeriesValue,
+      index: [
+        "2020-01-15T00:00:00",
+        "2020-02-15T00:00:00",
+        "2020-03-15T00:00:00",
+        "2020-04-15T00:00:00",
+      ],
+      calendar: "360_day",
+    };
+    const result = createChartData([nonStandardCalendarSeries], []);
+    expect(result.calendar).toBe("360_day");
+    // Still marked as a time axis via ISO-string detection; Date-based parsing
+    // remains approximate for 360_day/noleap calendars (see code comment).
+    expect(result.isTimeAxis).toBe(true);
+  });
+});
+
 describe("getDimensionKeys", () => {
   it("returns empty array for empty metadata", () => {
     expect(getDimensionKeys([])).toEqual([]);
diff --git a/frontend/src/components/execution/values/series/utils.ts b/frontend/src/components/execution/values/series/utils.ts
index 7637c68..c141377 100644
--- a/frontend/src/components/execution/values/series/utils.ts
+++ b/frontend/src/components/execution/values/series/utils.ts
@@ -166,10 +166,21 @@ export function createChartData(
   seriesMetadata: SeriesMetadata[];
   indexName: string;
   isTimeAxis: boolean;
+  valueUnits?: string;
+  indexUnits?: string;
+  calendar?: string;
 } {
-  // Deduplicate reference series by label (same observational data repeated across executions)
+  // Deduplicate reference series (same observational data repeated across executions).
+  // Prefer the reference_id content hash; fall back to label-based dedup only when
+  // reference_id is absent.
+  const seenRefIds = new Set<string>();
   const seenRefLabels = new Set<string>();
   const dedupedReferenceSeries = referenceSeriesValues.filter((series) => {
+    if (series.reference_id) {
+      if (seenRefIds.has(series.reference_id)) return false;
+      seenRefIds.add(series.reference_id);
+      return true;
+    }
     const label = applyLabelTemplate(series, labelTemplate);
     if (seenRefLabels.has(label)) return false;
     seenRefLabels.add(label);
@@ -191,17 +202,69 @@ export function createChartData(
   // Use the first series' index name
   const indexName = allSeries[0]?.index_name || "index";
 
-  // Detect if the index contains datetime strings
-  const firstIndex = allSeries[0]?.index ?? [];
-  const isTimeAxis = detectTimeAxis(firstIndex);
+  // Detect if the index contains datetime strings. All series are checked
+  // (not just the first) since alignment now happens by index value rather
+  // than by array position, and any series' index values can drive detection.
+  const isTimeAxis = allSeries.some((series) =>
+    detectTimeAxis(series.index ?? []),
+  );
+
+  // Prefer the per-series value_units for the Y-axis unit label (first series
+  // that has one); callers fall back to their own `units` prop when this is
+  // undefined, since ILAMB series never carry value_units.
+  const valueUnits =
+    allSeries.find((series) => series.value_units)?.value_units ?? undefined;
+  // Same fallback pattern for the X-axis unit label.
+  const indexUnits =
+    allSeries.find((series) => series.index_units)?.index_units ?? undefined;
+  // Calendar metadata (e.g. "standard", "360_day", "noleap"). ILAMB never
+  // sets this; ESMValTool does. Surfaced for callers/tooltips — see the
+  // code comment below on why non-standard calendars aren't fully honored.
+  const calendar =
+    allSeries.find((series) => series.calendar)?.calendar ?? undefined;
+
+  // Build the union of index values across all series, preserving first-seen
+  // order, so each series contributes its value to the row matching its own
+  // index value rather than being smeared across positions.
+  const indexValueOrder: (string | number)[] = [];
+  const seenIndexValues = new Set<string | number>();
+  for (const series of allSeries) {
+    for (const rawIndex of series.index ?? []) {
+      if (!seenIndexValues.has(rawIndex)) {
+        seenIndexValues.add(rawIndex);
+        indexValueOrder.push(rawIndex);
+      }
+    }
+  }
 
-  // Find the maximum length across all series
-  const maxLength = Math.max(...allSeries.map((s) => s.values?.length || 0));
+  // Order rows by index value so the line is drawn left-to-right regardless of
+  // the order the series were provided in (a first-seen union can interleave
+  // when series carry disjoint indices). Numeric indices sort numerically;
+  // string indices sort lexically, which is chronological for ISO-8601 dates.
+  indexValueOrder.sort((a, b) => {
+    if (typeof a === "number" && typeof b === "number") return a - b;
+    const [as, bs] = [String(a), String(b)];
+    return as < bs ? -1 : as > bs ? 1 : 0;
+  });
+
+  // Fallback when no series carries an index array at all: key rows by
+  // position (0, 1, 2, …) as the earlier implementation did, so index-less
+  // series still render instead of collapsing to an empty chart.
+  const hasAnyIndex = indexValueOrder.length > 0;
+  if (!hasAnyIndex) {
+    const maxLength = Math.max(
+      0,
+      ...allSeries.map((s) => s.values?.length ?? 0),
+    );
+    for (let i = 0; i < maxLength; i++) {
+      indexValueOrder.push(i);
+    }
+  }
 
   // Create series metadata
   const seriesMetadata: SeriesMetadata[] = allSeries.map((series, idx) => {
     const label = applyLabelTemplate(series, labelTemplate);
-    const isReference = idx >= seriesValues.length; // Deduplicated reference series come after regular series
+    const isReference = series.kind === "reference";
     const color = isReference ? "#000000" : getLabelColor(label);
     return {
       seriesIndex: idx,
@@ -212,30 +275,46 @@ export function createChartData(
     };
   });
 
-  // Build chart data
-  const chartData: ChartDataPoint[] = [];
-  for (let i = 0; i < maxLength; i++) {
-    const rawIndex = allSeries[0]?.index?.[i] ?? i;
-    let indexValue: number | string | null;
-    if (isTimeAxis && typeof rawIndex === "string") {
-      indexValue = parseDatetimeToTimestamp(rawIndex);
-    } else {
-      indexValue = rawIndex;
-    }
-    const dataPoint: ChartDataPoint = {
-      [indexName]: indexValue,
-    };
-
-    allSeries.forEach((series, seriesIdx) => {
-      if (series.values && i < series.values.length) {
-        dataPoint[`series_${seriesIdx}`] = series.values[i];
+  // Build chart data: one row per index value, in ascending order.
+  const chartData: ChartDataPoint[] = indexValueOrder.map(
+    (rawIndex, rowIdx) => {
+      let indexValue: number | string | null;
+      if (isTimeAxis && typeof rawIndex === "string") {
+        indexValue = parseDatetimeToTimestamp(rawIndex);
+      } else {
+        indexValue = rawIndex;
       }
-    });
+      const dataPoint: ChartDataPoint = {
+        [indexName]: indexValue,
+      };
 
-    chartData.push(dataPoint);
-  }
+      allSeries.forEach((series, seriesIdx) => {
+        if (!series.values) return;
+        // Series with an index align by value; index-less series fall back to
+        // positional lookup keyed off the row ordinal, so they still render even
+        // when other series in the chart carry an index.
+        const dataIdx =
+          series.index && series.index.length > 0
+            ? series.index.indexOf(rawIndex)
+            : rowIdx;
+        if (dataIdx !== -1 && dataIdx < series.values.length) {
+          dataPoint[`series_${seriesIdx}`] = series.values[dataIdx];
+        }
+      });
+
+      return dataPoint;
+    },
+  );
 
-  return { chartData, seriesMetadata, indexName, isTimeAxis };
+  return {
+    chartData,
+    seriesMetadata,
+    indexName,
+    isTimeAxis,
+    valueUnits,
+    indexUnits,
+    calendar,
+  };
 }
 
 /**
diff --git a/frontend/src/components/explorer/content/seriesChartContent.tsx b/frontend/src/components/explorer/content/seriesChartContent.tsx
index 813a67c..4d96cf9 100644
--- a/frontend/src/components/explorer/content/seriesChartContent.tsx
+++ b/frontend/src/components/explorer/content/seriesChartContent.tsx
@@ -117,13 +117,13 @@ export function SeriesChartContent({ contentItem }: SeriesChartContentProps) {
     isSeriesValue,
   ) as SeriesValue[];
 
-  // Split into regular and reference series
-  // TODO: support other times
+  // Split into regular and reference series based on role (kind).
+  // Missing/"model" kind is treated as a model series; only "reference" is a reference series.
   const regularSeries = allSeriesValues.filter(
-    (series) => series.dimensions.source_id !== "Reference",
+    (series) => series.kind !== "reference",
   );
   const referenceSeries = allSeriesValues.filter(
-    (series) => series.dimensions.source_id === "Reference",
+    (series) => series.kind === "reference",
   );
 
   // Auto-select the first available facet value for filter controls that
diff --git a/frontend/src/components/explorer/grouping/types.ts b/frontend/src/components/explorer/grouping/types.ts
index 764a8d2..8d4609a 100644
--- a/frontend/src/components/explorer/grouping/types.ts
+++ b/frontend/src/components/explorer/grouping/types.ts
@@ -40,4 +40,6 @@ export interface AvailableDimensions {
  */
 export interface DimensionedData {
   dimensions: Record<string, string>;
+  /** Role of this item ("model" or "reference"); absent/undefined means "model" */
+  kind?: "model" | "reference" | null;
 }
diff --git a/frontend/src/components/explorer/grouping/utils.test.ts b/frontend/src/components/explorer/grouping/utils.test.ts
index 9479a04..b150af8 100644
--- a/frontend/src/components/explorer/grouping/utils.test.ts
+++ b/frontend/src/components/explorer/grouping/utils.test.ts
@@ -239,17 +239,33 @@ describe("categorizeByGrouping", () => {
 });
 
 describe("isReferenceItem", () => {
-  it("returns true when source_id is Reference", () => {
-    const result = isReferenceItem(mockData[2]);
+  it("returns true when kind is reference", () => {
+    const item: DimensionedData = {
+      dimensions: { source_id: "ModelA" },
+      kind: "reference",
+    };
+    const result = isReferenceItem(item);
     expect(result).toBe(true);
   });
 
-  it("returns false when source_id is not Reference", () => {
-    const result = isReferenceItem(mockData[0]);
+  it("returns false when kind is model", () => {
+    const item: DimensionedData = {
+      dimensions: { source_id: "Reference" },
+      kind: "model",
+    };
+    const result = isReferenceItem(item);
+    expect(result).toBe(false);
+  });
+
+  it("returns false when kind is missing, even if source_id is Reference", () => {
+    const item: DimensionedData = {
+      dimensions: { source_id: "Reference" },
+    };
+    const result = isReferenceItem(item);
     expect(result).toBe(false);
   });
 
-  it("returns false when source_id is missing", () => {
+  it("returns false when both kind and source_id are missing", () => {
     const emptyItem: DimensionedData = { dimensions: {} };
     const result = isReferenceItem(emptyItem);
     expect(result).toBe(false);
diff --git a/frontend/src/components/explorer/grouping/utils.ts b/frontend/src/components/explorer/grouping/utils.ts
index 93df2bd..05bd2a3 100644
--- a/frontend/src/components/explorer/grouping/utils.ts
+++ b/frontend/src/components/explorer/grouping/utils.ts
@@ -162,10 +162,10 @@ export function categorizeByGrouping<T extends DimensionedData>(
 }
 
 /**
- * Check if an item is a reference item (based on source_id)
+ * Check if an item is a reference item (based on kind)
  */
 export function isReferenceItem<T extends DimensionedData>(item: T): boolean {
-  return item.dimensions.source_id === "Reference";
+  return item.kind === "reference";
 }
 
 /**