diff --git a/docs/source/conf.py b/docs/source/conf.py
index f9eaafcb..3afb88bb 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -14,6 +14,11 @@
 import sys
 import os
 
+# esgf is not currently available for Python 3 and will throw an
+# error when building the documents.
+if sys.version_info[0] >= 3:
+    autodoc_mock_imports = ["esgf"]
+
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
@@ -33,11 +38,15 @@
 
 # Add any Sphinx extension module names here, as strings. They can be extensions
 # coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
+
+# Note that 'sphinxcontrib.autohttp.bottle' is currently broken in Sphinx > 1.56
+# Remove from the extension list if documentation fails on Sphinx hard failure.
 extensions = [
     'sphinx.ext.autodoc',
     'sphinx.ext.intersphinx',
     'sphinx.ext.ifconfig',
     'sphinxcontrib.httpdomain',
+    'sphinxcontrib.autohttp.bottle'
 ]
 
 # Add any paths that contain templates here, relative to this directory.
diff --git a/ocw/dataset.py b/ocw/dataset.py
index 196913a1..0a0e1a6e 100644
--- a/ocw/dataset.py
+++ b/ocw/dataset.py
@@ -117,6 +117,7 @@ def spatial_resolution(self):
 
            If self.lats and self.lons are from curvilinear coordinates,
            the output resolutions are approximate values.
+
         :returns: The Dataset's latitudinal and longitudinal spatial resolution
             as a tuple of the form (lat_resolution, lon_resolution).
         :rtype: (:class:`float`, :class:`float`)
@@ -264,7 +265,7 @@ def __init__(self, boundary_type='rectangular',
                  start=None, end=None):
         '''Default Bounds constructor
         :param boundary_type: The type of spatial subset boundary.
-        :type boundary_type: :mod:`string'
+        :type boundary_type: :mod:`string`
 
 
         :param lat_min: The minimum latitude bound.
diff --git a/ocw/dataset_loader.py b/ocw/dataset_loader.py
index 4b2a9257..f84bdc40 100644
--- a/ocw/dataset_loader.py
+++ b/ocw/dataset_loader.py
@@ -33,33 +33,31 @@ def __init__(self, *loader_opts):
 
         Each keyword argument can be information for a dataset in dictionary
         form. For example:
-        ``
+
         >>> loader_opt1 = {'loader_name': 'rcmed', 'name': 'cru',
                            'dataset_id': 10, 'parameter_id': 34}
         >>> loader_opt2 = {'path': './data/TRMM_v7_3B43_1980-2010.nc,
                            'variable': 'pcp'}
         >>> loader = DatasetLoader(loader_opt1, loader_opt2)
-        ``
 
         Or more conveniently if the loader configuration is defined in a
         yaml file named config_file (see RCMES examples):
-        ``
+
         >>> import yaml
         >>> config = yaml.load(open(config_file))
         >>> obs_loader_config = config['datasets']['reference']
         >>> loader = DatasetLoader(*obs_loader_config)
-        ``
 
         As shown in the first example, the dictionary for each argument should
         contain a loader name and parameters specific to the particular loader.
         Once the configuration is entered, the datasets may be loaded using:
-        ``
+
         >>> loader.load_datasets()
         >>> obs_datasets = loader.datasets
-        ``
 
         Additionally, each dataset must have a ``loader_name`` keyword. This may
         be one of the following:
+
         * ``'local'`` - One or multiple dataset files in a local directory
         * ``'local_split'`` - A single dataset split accross multiple files in a
                               local directory
@@ -74,6 +72,7 @@ def __init__(self, *loader_opts):
         Users who wish to load datasets from loaders not described above may
         define their own custom dataset loader function and incorporate it as
         follows:
+
         >>> loader.add_source_loader('my_loader_name', my_loader_func)
 
         :param loader_opts: Dictionaries containing the each dataset loader
@@ -84,7 +83,7 @@ def __init__(self, *loader_opts):
         :type loader_opts: :class:`dict`
 
         :raises KeyError: If an invalid argument is passed to a data source
-        loader function.
+                            loader function.
         '''
         # dataset loader config
         self.set_loader_opts(*loader_opts)
@@ -115,8 +114,8 @@ def add_source_loader(self, loader_name, loader_func):
         :type loader_name: :mod:`string`
 
         :param loader_func: Reference to a custom defined function. This should
-        return an OCW Dataset object, and have an origin which satisfies
-        origin['source'] == loader_name.
+                            return an OCW Dataset object, and have an origin which satisfies
+                            origin['source'] == loader_name.
         :type loader_func: :class:`callable`
         '''
         self._source_loaders[loader_name] = loader_func
diff --git a/ocw/utils.py b/ocw/utils.py
index c2b62cfe..8f2c8c14 100755
--- a/ocw/utils.py
+++ b/ocw/utils.py
@@ -402,7 +402,7 @@ def trim_dataset(dataset):
     ''' Trim datasets such that first and last year of data have all 12 months
 
     :param dataset: Dataset object
-    :type dataset: :class:`dataset.Dataset
+    :type dataset: :class:`dataset.Dataset`
 
     :returns: Slice index for trimmed dataset
     '''
@@ -653,7 +653,7 @@ def _force_unicode(s, encoding='utf-8'):
 def calculate_temporal_trends(dataset):
     ''' Calculate temporal trends in dataset.values
     :param dataset: The dataset from which time values should be extracted.
-    :type dataset: :class:`dataset.Dataset'
+    :type dataset: :class:`dataset.Dataset`
 
     :returns: Arrays of the temporal trend and standard error
     :rtype: :class:`numpy.ma.core.MaskedArray`
@@ -675,13 +675,13 @@ def calculate_temporal_trends(dataset):
 def calculate_ensemble_temporal_trends(timeseries_array, number_of_samples=1000):
     ''' Calculate temporal trends in an ensemble of time series
     :param timeseries_array: Two dimensional array. 1st index: model, 2nd index: time.
-    :type timeseries_array: :class:`numpy.ndarray'
+    :type timeseries_array: :class:`numpy.ndarray`
 
     :param sampling: A list whose elements are one-dimensional numpy arrays
-    :type timeseries_array: :class:`list'
+    :type timeseries_array: :class:`list`
 
     :returns: temporal trend and estimated error from bootstrapping
-    :rtype: :float:`float','float'
+    :rtype: :class:`float`, :class:`float`
     '''
 
     nmodels, nt = timeseries_array.shape
@@ -701,13 +701,13 @@ def calculate_ensemble_temporal_trends(timeseries_array, number_of_samples=1000)
 def calculate_temporal_trend_of_time_series(x,y):
     ''' Calculate least-square trends (a) in y = ax+b and a's standard error
     :param x: time series
-    :type x: :class:`numpy.ndarray'
+    :type x: :class:`numpy.ndarray`
 
     :param x: time series
-    :type x: :class:`numpy.ndarray'
+    :type x: :class:`numpy.ndarray`
 
     :returns: temporal trend and standard error
-    :rtype: :float:`float','float'
+    :rtype: :class:`float`, :class:`float`
     '''
     slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
     return slope, std_err