diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index d25736f1a9ca47984ac513805a9b458ff09ff667..c3fb7abc9c51378552ed91d2fa6b69e08fa351e7 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -1195,12 +1195,14 @@ class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass):  # pragma: no cover
             ax.set_ylim([ylims[0], ylims[1]*1.025])
             ax.set_ylabel(f"{self.error_measure} (in {self.error_unit})")
             ax.set_xticklabels(ax.get_xticklabels(), rotation=45)
+            ax.set_xlabel(None)
         elif orientation == "h":
             if apply_u_test:
                 ax = self.set_significance_bars(asteriks, ax, data_table, orientation)
             ax.set_xlabel(f"{self.error_measure} (in {self.error_unit})")
             xlims = list(ax.get_xlim())
             ax.set_xlim([xlims[0], xlims[1] * 1.015])
+            ax.set_ylabel(None)
         else:
             raise ValueError(f"orientation must be `v' or `h' but is: {orientation}")
         text = f"n={n_boots}"
@@ -1323,7 +1325,8 @@ class PlotTimeEvolutionMetric(AbstractPlotClass):
 class PlotSeasonalMSEStack(AbstractPlotClass):
 
     def __init__(self, data_path: str, plot_folder: str = ".", boot_dim="boots", ahead_dim="ahead",
-                 sampling: str = "daily", error_measure: str = "MSE", error_unit: str = "ppb$^2$"):
+                 sampling: str = "daily", error_measure: str = "MSE", error_unit: str = "ppb$^2$",
+                 model_name: str = "NN", model_indicator: str = "nn", model_type_dim: str = "type"):
         """Set attributes and create plot."""
         super().__init__(plot_folder, "seasonal_mse_stack_plot")
         self.plot_name_orig = "seasonal_mse_stack_plot"
@@ -1331,13 +1334,13 @@ class PlotSeasonalMSEStack(AbstractPlotClass):
         self.season_dim = "season"
         self.error_unit = error_unit
         self.error_measure = error_measure
-        self._data = self._prepare_data(boot_dim, data_path)
+        self._data = self._prepare_data(boot_dim, data_path, model_type_dim, model_indicator, model_name)
         for orientation in ["horizontal", "vertical"]:
             for split_ahead in [True, False]:
                 self._plot(ahead_dim, split_ahead, sampling, orientation)
                 self._save(bbox_inches="tight")
 
-    def _prepare_data(self, boot_dim, data_path):
+    def _prepare_data(self, boot_dim, data_path, model_type_dim, model_indicator, model_name):
         season_dim = self.season_dim
         data = {}
         for season in ["total", "DJF", "MAM", "JJA", "SON"]:
@@ -1353,7 +1356,9 @@ class PlotSeasonalMSEStack(AbstractPlotClass):
         xr_data = xr.Dataset(mean).to_array(season_dim)
         factor = xr_data.sel({season_dim: "total"}) / xr_data.sel({season_dim: ["DJF", "MAM", "JJA", "SON"]}).sum(
             season_dim)
-        return xr_data.sel({season_dim: ["DJF", "MAM", "JJA", "SON"]}) * factor
+        xr_data = xr_data.sel({season_dim: ["DJF", "MAM", "JJA", "SON"]}) * factor
+        xr_data[model_type_dim] = [v if v != model_indicator else model_name for v in xr_data[model_type_dim].values]
+        return xr_data
 
     @staticmethod
     def _get_target_sampling(sampling, pos):
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index d65a200161a7593fe03df5053328aa3f8cd77310..de58e9054aa1619ddb5b8fd1fb481b25bf089f5b 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -686,7 +686,9 @@ class PostProcessing(RunEnvironment):
                 report_path = os.path.join(self.data_store.get("experiment_path"), "latex_report")
                 PlotSeasonalMSEStack(data_path=report_path, plot_folder=self.plot_path,
                                      boot_dim=self.uncertainty_estimate_boot_dim, ahead_dim=self.ahead_dim,
-                                     sampling=self._sampling, error_measure="Mean Squared Error", error_unit=r"ppb$^2$")
+                                     sampling=self._sampling, error_measure="Mean Squared Error", error_unit=r"ppb$^2$",
+                                     model_indicator=self.forecast_indicator, model_name=self.model_display_name,
+                                     model_type_dim=self.model_type_dim)
         except Exception as e:
             logging.error(f"Could not create plot PlotSeasonalMSEStack due to the following error: {e}"
                           f"\n{sys.exc_info()[0]}\n{sys.exc_info()[1]}\n{sys.exc_info()[2]}")