diff --git a/mlair/configuration/defaults.py b/mlair/configuration/defaults.py
index 9dc252b89455a3f18c995fa7992c51fbc1476a57..bff85e4727437d33b7c07228713405ad252e64ad 100644
--- a/mlair/configuration/defaults.py
+++ b/mlair/configuration/defaults.py
@@ -54,7 +54,8 @@ DEFAULT_BOOTSTRAP_TYPE = "singleinput"
 DEFAULT_BOOTSTRAP_METHOD = "shuffle"
 DEFAULT_PLOT_LIST = ["PlotMonthlySummary", "PlotStationMap", "PlotClimatologicalSkillScore", "PlotTimeSeries",
                      "PlotCompetitiveSkillScore", "PlotBootstrapSkillScore", "PlotConditionalQuantiles",
-                     "PlotAvailability", "PlotAvailabilityHistogram", "PlotDataHistogram", "PlotPeriodogram"]
+                     "PlotAvailability", "PlotAvailabilityHistogram", "PlotDataHistogram", "PlotPeriodogram",
+                     "PlotSampleUncertaintyFromBootstrap"]
 DEFAULT_SAMPLING = "daily"
 DEFAULT_DATA_ORIGIN = {"cloudcover": "REA", "humidity": "REA", "pblheight": "REA", "press": "REA", "relhum": "REA",
                        "temp": "REA", "totprecip": "REA", "u": "REA", "v": "REA", "no": "", "no2": "", "o3": "",
diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index 5e8b121ae26ed0d4a967b9c440071d61c1ad703b..ae531b2526dee93672ab989d621d92eba541f19f 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -998,6 +998,34 @@ class PlotSeparationOfScales(AbstractPlotClass):
             self._save()
 
 
+class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass):
+
+    def __init__(self, data: xr.DataArray, plot_folder: str = ".", model_type_dim: str = "type",
+                 error_measure: str = "mse", dim_name_boots: str = 'boots'):
+        plot_folder = os.path.join(plot_folder)
+        super().__init__(plot_folder, "sample_uncertainty_from_bootstrap")
+        self.model_type_dim = model_type_dim
+        self.error_measure = error_measure
+        self.dim_name_boots = dim_name_boots
+
+        self._plot(data)
+
+    def _plot(self, data):
+        data_table = data.to_pandas()
+        size = max([len(np.unique(data_table.columns)), 6])
+        fig, ax = plt.subplots(figsize=(size, size * 0.8))
+        # fig, ax = plt.subplots()
+        sns.boxplot(data=data_table, ax=ax, whis=1., color="white",
+                    showmeans=True, meanprops={"markersize": 3, "markeredgecolor": "k"},
+                    flierprops={"marker": ".", "markerfacecolor": 'black', "markeredgecolor": 'none', "markersize": 1},
+                    width=.3)
+        ax.set_ylabel(f"{self.error_measure} " + r" in ppb$^2$")
+        ax.set_xticklabels(ax.get_xticklabels(), rotation=45)
+        plt.tight_layout()
+        self._save()
+# a = xr.DataArray(np.array(range(20)).reshape(2,-1).T, dims={'time':range(10), 'model': ['m1', 'm2']}, coords={'time':range(10), 'model': ['m1', 'm2']})
+# create_n_bootstrap_realizations(a, dim_name_time='time', dim_name_model='model', n_boots=100)
+
 if __name__ == "__main__":
     stations = ['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087']
     path = "../../testrun_network/forecasts"
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index e580a2b3acd04f4f8e82154d9e3f16000d75c84b..e9110b43273894365b7ef336f16096944a4a5c10 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -22,7 +22,8 @@ from mlair.helpers import TimeTracking, statistics, extract_value, remove_items,
 from mlair.model_modules.linear_model import OrdinaryLeastSquaredModel
 from mlair.model_modules import AbstractModelClass
 from mlair.plotting.postprocessing_plotting import PlotMonthlySummary, PlotClimatologicalSkillScore, \
-    PlotCompetitiveSkillScore, PlotTimeSeries, PlotBootstrapSkillScore, PlotConditionalQuantiles, PlotSeparationOfScales
+    PlotCompetitiveSkillScore, PlotTimeSeries, PlotBootstrapSkillScore, PlotConditionalQuantiles, \
+    PlotSeparationOfScales, PlotSampleUncertaintyFromBootstrap
 from mlair.plotting.data_insight_plotting import PlotStationMap, PlotAvailability, PlotAvailabilityHistogram, \
     PlotPeriodogram, PlotDataHistogram
 from mlair.run_modules.run_environment import RunEnvironment
@@ -535,6 +536,14 @@ class PostProcessing(RunEnvironment):
             logging.error(f"Could not create plot PlotDataHistogram due to the following error: {e}"
                           f"\n{sys.exc_info()[0]}\n{sys.exc_info()[1]}\n{sys.exc_info()[2]}")
 
+        try:
+            if "PlotSampleUncertaintyFromBootstrap" in plot_list:
+                PlotSampleUncertaintyFromBootstrap(data=None, plot_folder=self.plot_path,
+                                                   model_type_dim=self.model_type_dim)
+        except Exception as e:
+            logging.error(f"Could not create plot PlotSampleUncertaintyFromBootstrap due to the following error: {e}"
+                          f"\n{sys.exc_info()[0]}\n{sys.exc_info()[1]}\n{sys.exc_info()[2]}")
+
     def calculate_test_score(self):
         """Evaluate test score of model and save locally."""