diff --git a/ema_workbench/em_framework/callbacks.py b/ema_workbench/em_framework/callbacks.py
index 8e0ca9e..4f1ee72 100644
--- a/ema_workbench/em_framework/callbacks.py
+++ b/ema_workbench/em_framework/callbacks.py
@@ -232,7 +232,7 @@ class DefaultCallback(AbstractCallback):
             shape = outcome.shape
             if shape is not None:
                 shape = (nr_experiments,) + shape
-                self.results[outcome.name] = self._setup_outcomes_array(shape, dtype=float)
+                self.results[outcome.name] = self._setup_outcomes_array(shape, dtype=outcome.dtype)
 
     def _store_case(self, experiment):
         scenario = experiment.scenario
diff --git a/ema_workbench/em_framework/outcomes.py b/ema_workbench/em_framework/outcomes.py
index 0b0f52b..f7e2707 100644
--- a/ema_workbench/em_framework/outcomes.py
+++ b/ema_workbench/em_framework/outcomes.py
@@ -104,6 +104,11 @@ class AbstractOutcome(Variable):
                      expected min and max value for outcome,
                      used by HyperVolume convergence metric
     shape : {tuple, None} optional
+            must be used in conjunction with dtype. Enables pre-allocation
+            of data structure for storing results.
+    dtype : datatype, optional
+            must be used in conjunction with shape. Enables pre-allocation
+            of data structure for storing results.
 
     Attributes
     ----------
@@ -112,6 +117,7 @@ class AbstractOutcome(Variable):
     variable_name : str
     function : callable
     shape : tuple
+    dtype : dataype
 
     """
 
@@ -122,7 +128,14 @@ class AbstractOutcome(Variable):
     INFO = 0
 
     def __init__(
-        self, name, kind=INFO, variable_name=None, function=None, expected_range=None, shape=None
+        self,
+        name,
+        kind=INFO,
+        variable_name=None,
+        function=None,
+        expected_range=None,
+        shape=None,
+        dtype=None,
     ):
         super().__init__(name)
 
@@ -135,6 +148,8 @@ class AbstractOutcome(Variable):
                 raise ValueError("variable name must be a string or list of strings")
         if expected_range is not None and len(expected_range) != 2:
             raise ValueError("expected_range must be a min-max tuple")
+        if (shape is not None and dtype is None) or (dtype is not None and shape is None):
+            raise ValueError("if using shape or dtype, both need to be provided")
 
         register(self)
 
@@ -151,6 +166,7 @@ class AbstractOutcome(Variable):
         self.function = function
         self._expected_range = expected_range
         self.shape = shape
+        self.dtype = dtype
 
     def process(self, values):
         if self.function:
@@ -270,6 +286,8 @@ class ScalarOutcome(AbstractOutcome):
     expected_range : collection, optional
                      expected min and max value for outcome,
                      used by HyperVolume convergence metric
+    dtype : datatype, optional
+            Enables pre-allocation of data structure for storing results.
 
     Attributes
     ----------
@@ -279,6 +297,7 @@ class ScalarOutcome(AbstractOutcome):
     function : callable
     shape : tuple
     expected_range : tuple
+    dtype : datatype
 
     """
 
@@ -299,8 +318,14 @@ class ScalarOutcome(AbstractOutcome):
         variable_name=None,
         function=None,
         expected_range=None,
+        dtype=None,
     ):
-        super().__init__(name, kind, variable_name=variable_name, function=function)
+        shape = None
+        if dtype is not None:
+            shape = (1,)
+        super().__init__(
+            name, kind, variable_name=variable_name, function=function, dtype=dtype, shape=shape
+        )
         self.expected_range = expected_range
 
     def process(self, values):
@@ -360,7 +385,12 @@ class ArrayOutcome(AbstractOutcome):
     expected_range : 2 tuple, optional
                      expected min and max value for outcome,
                      used by HyperVolume convergence metric
-    shape : {tuple, None}, optional
+    shape : {tuple, None} optional
+            must be used in conjunction with dtype. Enables pre-allocation
+            of data structure for storing results.
+    dtype : datatype, optional
+            must be used in conjunction with shape. Enables pre-allocation
+            of data structure for storing results.
 
     Attributes
     ----------
@@ -370,17 +400,21 @@ class ArrayOutcome(AbstractOutcome):
     function : callable
     shape : tuple
     expected_range : tuple
+    dtype : datatype
 
 
     """
 
-    def __init__(self, name, variable_name=None, function=None, expected_range=None, shape=None):
+    def __init__(
+        self, name, variable_name=None, function=None, expected_range=None, shape=None, dtype=None
+    ):
         super().__init__(
             name,
             variable_name=variable_name,
             function=function,
             expected_range=expected_range,
             shape=shape,
+            dtype=dtype,
         )
 
     def process(self, values):
@@ -450,7 +484,12 @@ class TimeSeriesOutcome(ArrayOutcome):
     expected_range : 2 tuple, optional
                      expected min and max value for outcome,
                      used by HyperVolume convergence metric
-    shape : {tuple, None}, optional
+    shape : {tuple, None} optional
+            must be used in conjunction with dtype. Enables pre-allocation
+            of data structure for storing results.
+    dtype : datatype, optional
+            must be used in conjunction with shape. Enables pre-allocation
+            of data structure for storing results.
 
     Attributes
     ----------
@@ -460,6 +499,7 @@ class TimeSeriesOutcome(ArrayOutcome):
     function : callable
     shape : tuple
     expected_range : tuple
+    dtype : datatype
 
     Notes
     -----
@@ -469,13 +509,16 @@ class TimeSeriesOutcome(ArrayOutcome):
 
     """
 
-    def __init__(self, name, variable_name=None, function=None, expected_range=None, shape=None):
+    def __init__(
+        self, name, variable_name=None, function=None, expected_range=None, shape=None, dtype=None
+    ):
         super().__init__(
             name,
             variable_name=variable_name,
             function=function,
             expected_range=expected_range,
             shape=shape,
+            dtype=dtype,
         )
 
     @classmethod

diff --git a/test/test_em_framework/test_callback.py b/test/test_em_framework/test_callback.py
index aa5ceaa..b0663a6 100644
--- a/test/test_em_framework/test_callback.py
+++ b/test/test_em_framework/test_callback.py
@@ -81,7 +81,7 @@ def test_init():
     uncs = [RealParameter("a", 0, 1), RealParameter("b", 0, 1)]
     outcomes = [
         ScalarOutcome("scalar"),
-        ArrayOutcome("array", shape=(10,)),
+        ArrayOutcome("array", shape=(10,), dtype=float),
         TimeSeriesOutcome("timeseries"),
     ]
     callback = DefaultCallback(uncs, [], outcomes, nr_experiments=100)
diff --git a/test/test_em_framework/test_outcomes.py b/test/test_em_framework/test_outcomes.py
index dc32baf..fc6b1b8 100644
--- a/test/test_em_framework/test_outcomes.py
+++ b/test/test_em_framework/test_outcomes.py
@@ -153,6 +153,14 @@ class TestTimeSeriesOutcome(TestScalarOutcome):
 
             outcome.process([1])
 
+        with self.assertRaises(ValueError):
+            name = "f"
+            outcome = self.outcome_class(name, function=function, shape=(1,))
+
+        with self.assertRaises(ValueError):
+            name = "f"
+            outcome = self.outcome_class(name, function=function, dtype=float)
+
 
 class CreateOutcomesTestCase(unittest.TestCase):
     def test_create_outcomes(self):

enable passing shape as keyword argument in outcomes
Currently, the callback sets up the data structures for storing results after the first experiment because only then the exact required shapes are known. By passing the shape as an optional keyword argument (for array and time series outcomes), it becomes possible to pre-allocate these before the first experiment. This also allows catching memory errors before the first run is even conducted. The basic machinery for this is already in place in both AbstractOutcome and the DefaultCallback, the feature, however, was never fully completed.

Thinking about this a bit more, I do believe you need to pass both shape and dtype in order to be able to setup the datastructure in advance.
 
test/test_em_framework/test_callback.py::test_init test/test_em_framework/test_outcomes.py::TestTimeSeriesOutcome::test_process
test/test_em_framework/test_callback.py::test_store_results test/test_em_framework/test_callback.py::test_store_cases test/test_em_framework/test_callback.py::test_get_results test/test_em_framework/test_callback.py::test_filebasedcallback test/test_em_framework/test_outcomes.py::TestScalarOutcome::test_outcome test/test_em_framework/test_outcomes.py::TestScalarOutcome::test_process test/test_em_framework/test_outcomes.py::TestTimeSeriesOutcome::test_outcome test/test_em_framework/test_outcomes.py::CreateOutcomesTestCase::test_create_outcomes