diff --git a/act/qc/qctests.py b/act/qc/qctests.py
index efdb0f205b..b4e619d240 100644
--- a/act/qc/qctests.py
+++ b/act/qc/qctests.py
@@ -1693,38 +1693,39 @@ def add_step_change_test(
 
         """
 
-        def cusum(data, k, mean_val, lower=False):
+        def cusum(data, k):
             """
-            CUSUM algrithm used to detect step changes.
+            CUSUM algorithm used to detect step changes.
 
             data : numpy array
                 1D numpy array of time series data to analze
             k : float
                 Reference value. This is typically half the value of the shift size change to
-                to be detected.
-            mean_val : float
-                Mean value of data.
-            lower : bool
-                Option to loof for lower shifts vs. upper shifts
+                to be detected or the size of the shift change if the data is detrended by
+                differencing.
+
+            Returns
+            -------
+            C : numpy array
+                Numpy array containing a 0 when there is no shift detected
+                or positive value when a shift is detected.
 
             """
 
-            C = np.zeros(data.size)
-            if lower:
-                for ii in range(1, data.size):
-                    C[ii] = max(0, C[ii - 1] - (data[ii] - mean_val + k))
-            else:
-                for ii in range(1, data.size):
-                    C[ii] = max(0, C[ii - 1] + (data[ii] - mean_val - k))
+            mean_val = np.nanmean(data)
+            Cu = np.zeros(data.size, dtype=np.float16)
+            Cl = np.zeros(data.size, dtype=np.float16)
+            for ii in range(1, data.size):
+                Cl[ii] = max(0.0, Cl[ii - 1] - (data[ii] - mean_val + k))
+                Cu[ii] = max(0.0, Cu[ii - 1] + (data[ii] - mean_val - k))
 
-            return C
+            return np.maximum(Cu, Cl)
 
         data = self._ds[var_name].values
-        time = self._ds['time'].values
         if add_nan:
             from act.utils.data_utils import add_in_nan
 
-            time, data = add_in_nan(time, data)
+            time, data = add_in_nan(self._ds['time'].values, data)
 
         data = data.astype(float)
         if detrend:
@@ -1734,29 +1735,27 @@ def cusum(data, k, mean_val, lower=False):
         if n_flagged < 0:
             n_flagged = data.size
 
-        mean_val = np.nanmean(data)
         index = np.full(data.size, False)
-        for lower in [False, True]:
-            C = cusum(data, k, mean_val, lower=lower)
-            found_ind = np.where(np.diff(C) > 0.0)[0]
-            for ind in found_ind:
-                ind = np.arange(ind, ind + n_flagged)
-                ind = ind[ind < data.size]
-                index[ind] = True
+        C = cusum(data, k)
+        found_ind = np.where(np.diff(C) > 0.0)[0]
+        for ind in found_ind:
+            ind = np.arange(ind, ind + n_flagged)
+            ind = ind[ind < data.size]
+            index[ind] = True
 
         if add_nan:
             import xarray as xr
 
             da = xr.DataArray(index, dims=['time'], coords=[time])
-            result = da.sel(time=self._ds['time'])
-            index = result.values
-            del result
+            da = da.sel(time=self._ds['time'])
+            index = da.values
+            del da
 
         if test_meaning is None:
-            test_meaning = f'Shift in data detected with CUSUM algrithm: k={round(k, 2)}'
+            test_meaning = f'Shift in data detected with CUSUM algorithm: k={round(k, 2)}'
 
         if prepend_text is not None:
-            test_meaning = ': '.join((prepend_text, test_meaning))
+            test_meaning = f'{prepend_text}: {test_meaning}'
 
         result = self._ds.qcfilter.add_test(
             var_name,