diff --git a/adapt_common/__init__.py b/adapt_common/__init__.py
index 2c8fc83..ba76075 100644
--- a/adapt_common/__init__.py
+++ b/adapt_common/__init__.py
@@ -1 +1,3 @@
+from adapt_common.norms import *  # noqa
 from adapt_common.reduction import *  # noqa
+from adapt_common.utility import *  # noqa
diff --git a/adapt_common/norms.py b/adapt_common/norms.py
new file mode 100644
index 0000000..cd1f49d
--- /dev/null
+++ b/adapt_common/norms.py
@@ -0,0 +1,163 @@
+"""Module containing overloaded versions of Firedrake's norm and errornorm functions."""
+
+import firedrake as fd
+import ufl
+from firedrake.petsc import PETSc
+
+from adapt_common.utility import cofunction2function
+
+__all__ = ["errornorm", "norm"]
+
+
+@PETSc.Log.EventDecorator()
+def norm(v, norm_type="L2", condition=None, boundary=False):
+    r"""Overload :func:`fd.norms.norm` to allow for :math:`\ell^p` norms.
+
+    Currently supported ``norm_type`` options:
+    * ``'l1'``
+    * ``'l2'``
+    * ``'linf'``
+    * ``'L2'``
+    * ``'Linf'``
+    * ``'H1'``
+    * ``'Hdiv'``
+    * ``'Hcurl'``
+    * or any ``'Lp'`` with :math:`p >= 1`.
+
+    Note that this version is case sensitive, i.e. ``'l2'`` and ``'L2'`` will give
+    different results in general.
+
+    :arg v: the function to take the norm of
+    :type v: :class:`fd.function.Function` or
+        :class:`fd.cofunction.Cofunction`
+    :kwarg norm_type: the type of norm to use
+    :type norm_type: :class:`str`
+    :kwarg condition: a UFL condition for specifying a subdomain to compute the norm
+        over
+    :kwarg boundary: if ``True``, the norm is computed over the domain boundary
+    :type boundary: :class:`bool`
+    :returns: the norm value
+    :rtype: :class:`float`
+    """
+    if isinstance(v, fd.Cofunction):
+        v = cofunction2function(v)
+    condition = condition or fd.Constant(1.0)
+    norm_codes = {"l1": 0, "l2": 2, "linf": 3}
+    p = 2
+    if norm_type in norm_codes or norm_type == "Linf":
+        if boundary:
+            not_impl_err = "lp errors on the boundary not yet implemented."
+            raise NotImplementedError(not_impl_err)
+        v.interpolate(condition * v)
+        with v.dat.vec_ro as vv:
+            if norm_type == "Linf":
+                return vv.max()[1]
+            else:
+                return vv.norm(norm_codes[norm_type])
+    elif norm_type[0] == "l":
+        not_impl_err = f"lp norm of order {norm_type[1:]} not supported."
+        raise NotImplementedError(not_impl_err)
+    else:
+        dX = ufl.ds if boundary else ufl.dx
+        if norm_type.startswith("L"):
+            try:
+                p = int(norm_type[1:])
+            except ValueError as exc:
+                val_err = f"Unable to interpret '{norm_type}' norm."
+                raise ValueError(val_err) from exc
+            if p < 1:
+                val_err = f"'{norm_type}' norm does not make sense."
+                raise ValueError(val_err)
+            integrand = ufl.inner(v, v)
+        elif norm_type.lower() in ("h1", "hdiv", "hcurl"):
+            integrand = {
+                "h1": lambda w: ufl.inner(w, w) + ufl.inner(ufl.grad(w), ufl.grad(w)),
+                "hdiv": lambda w: ufl.inner(w, w) + ufl.div(w) * ufl.div(w),
+                "hcurl": lambda w: ufl.inner(w, w)
+                + ufl.inner(ufl.curl(w), ufl.curl(w)),
+            }[norm_type.lower()](v)
+        else:
+            val_err = f"Unknown norm type '{norm_type}'."
+            raise ValueError(val_err)
+        return fd.assemble(condition * integrand ** (p / 2) * dX) ** (1 / p)
+
+
+@PETSc.Log.EventDecorator()
+def errornorm(u, uh, norm_type="L2", boundary=False, **kwargs):
+    r"""Overload :func:`fd.norms.errornorm` to allow for :math:`\ell^p` norms.
+
+    Currently supported ``norm_type`` options:
+    * ``'l1'``
+    * ``'l2'``
+    * ``'linf'``
+    * ``'L2'``
+    * ``'Linf'``
+    * ``'H1'``
+    * ``'Hdiv'``
+    * ``'Hcurl'``
+    * or any ``'Lp'`` with :math:`p >= 1`.
+
+    Note that this version is case sensitive, i.e. ``'l2'`` and ``'L2'`` will give
+    different results in general.
+
+    :arg u: the 'true' value
+    :type u: :class:`fd.function.Function` or
+        :class:`fd.cofunction.Cofunction`
+    :arg uh: the approximation of the 'truth'
+    :type uh: :class:`fd.function.Function` or
+        :class:`fd.cofunction.Cofunction`
+    :kwarg norm_type: the type of norm to use
+    :type norm_type: :class:`str`
+    :kwarg boundary: if ``True``, the norm is computed over the domain boundary
+    :type boundary: :class:`bool`
+    :returns: the error norm value
+    :rtype: :class:`float`
+
+    Any other keyword arguments are passed to :func:`fd.norms.errornorm`.
+    """
+    if isinstance(u, fd.Cofunction):
+        u = cofunction2function(u)
+    if isinstance(uh, fd.Cofunction):
+        uh = cofunction2function(uh)
+    if not isinstance(uh, fd.Function):
+        type_err = f"uh should be a Function, is a '{type(uh)}'."
+        raise TypeError(type_err)
+    if norm_type[0] == "l" and not isinstance(u, fd.Function):
+        type_err = f"u should be a Function, is a '{type(u)}'."
+        raise TypeError(type_err)
+
+    if len(u.ufl_shape) != len(uh.ufl_shape):
+        val_err = "Mismatching rank between u and uh."
+        raise ValueError(val_err)
+
+    if isinstance(u, fd.Function):
+        degree_u = u.function_space().ufl_element().degree()
+        degree_uh = uh.function_space().ufl_element().degree()
+        if degree_uh > degree_u:
+            fd.logging.warning(
+                "Degree of exact solution less than approximation degree"
+            )
+
+    # Case 1: point-wise norms
+    if norm_type[0] == "l":
+        v = u
+        v -= uh
+
+    # Case 2: UFL norms for mixed function spaces
+    elif hasattr(uh.function_space(), "num_sub_spaces"):
+        if norm_type == "L2":
+            vv = [
+                uu - uuh
+                for uu, uuh in zip(u.subfunctions, uh.subfunctions, strict=False)
+            ]
+            dX = ufl.ds if boundary else ufl.dx
+            return ufl.sqrt(fd.assemble(sum([ufl.inner(v, v) for v in vv]) * dX))
+        else:
+            not_impl_err = f"Norm type '{norm_type}' not supported for mixed spaces."
+            raise NotImplementedError(not_impl_err)
+
+    # Case 3: UFL norms for non-mixed spaces
+    else:
+        v = u - uh
+
+    return norm(v, norm_type=norm_type, **kwargs)
diff --git a/test/test_norms.py b/test/test_norms.py
new file mode 100644
index 0000000..c979379
--- /dev/null
+++ b/test/test_norms.py
@@ -0,0 +1,111 @@
+"""Unit tests for overloaded norm and errornorm functions."""
+
+import firedrake as fd
+import numpy as np
+import pytest
+import ufl
+
+from adapt_common.norms import errornorm, norm
+
+
+@pytest.fixture(params=["L1", "L2", "L4", "H1", "HCurl"])
+def integral_norm_type(request):
+    """Fixture for integral norm types."""
+    return request.param
+
+
+@pytest.fixture(params=["L1", "L2", "L4", "H1", "HCurl", "l1", "l2", "linf"])
+def norm_type(request):
+    """Fixture for all norm types."""
+    return request.param
+
+
+@pytest.fixture
+def mesh():
+    """Create a simple unit square mesh for testing."""
+    return fd.UnitSquareMesh(4, 4)
+
+
+@pytest.fixture
+def scalar_function(mesh):
+    """Create a scalar function on the mesh for testing."""
+    x, y = ufl.SpatialCoordinate(mesh)
+    V = fd.FunctionSpace(mesh, "CG", 1)
+    return fd.Function(V).interpolate(x**2 + y)
+
+
+@pytest.fixture
+def vector_function(mesh):
+    """Create a vector function on the mesh for testing."""
+    x, y = ufl.SpatialCoordinate(mesh)
+    V = fd.VectorFunctionSpace(mesh, "CG", 1)
+    return fd.Function(V).interpolate(ufl.as_vector([y * y, -x * x]))
+
+
+def test_boundary_error(scalar_function):
+    """Test that boundary error raises NotImplementedError under lp norm."""
+    not_impl_err = "lp errors on the boundary not yet implemented."
+    with pytest.raises(NotImplementedError, match=not_impl_err):
+        norm(scalar_function, norm_type="l1", boundary=True)
+
+
+def test_l1(scalar_function):
+    """Test l1 norm computation."""
+    expected = np.sum(np.abs(scalar_function.dat.data))
+    got = norm(scalar_function, norm_type="l1")
+    assert np.isclose(expected, got)
+
+
+def test_l2(scalar_function):
+    """Test l2 norm computation."""
+    expected = np.sqrt(np.sum(scalar_function.dat.data**2))
+    got = norm(scalar_function, norm_type="l2")
+    assert np.isclose(expected, got)
+
+
+def test_linf(scalar_function):
+    """Test linf norm computation."""
+    expected = np.max(scalar_function.dat.data)
+    got = norm(scalar_function, norm_type="linf")
+    assert np.isclose(expected, got)
+
+
+def test_notimplemented_lp_error(scalar_function):
+    """Test that lp norm raises NotImplementedError."""
+    not_impl_err = "lp norm of order p not supported."
+    with pytest.raises(NotImplementedError, match=not_impl_err):
+        norm(scalar_function, norm_type="lp")
+
+
+def test_invalid_norm_type_error(scalar_function):
+    """Test that invalid norm type raises ValueError."""
+    val_err = "Unknown norm type 'X'."
+    with pytest.raises(ValueError, match=val_err):
+        norm(scalar_function, norm_type="X")
+
+
+def test_consistency_firedrake(scalar_function, integral_norm_type):
+    """Test consistency with Firedrake's norm implementation."""
+    expected = fd.norm(scalar_function, norm_type=integral_norm_type)
+    got = norm(scalar_function, norm_type=integral_norm_type)
+    assert np.isclose(expected, got)
+
+
+def test_zero_scalar(scalar_function, norm_type):
+    """Test that errornorm returns zero for identical scalar functions."""
+    err = errornorm(scalar_function, scalar_function, norm_type=norm_type)
+    assert np.isclose(err, 0.0)
+
+
+def test_consistency_errornorm(scalar_function, integral_norm_type):
+    """Test consistency of errornorm with Firedrake's implementation."""
+    g = fd.Function(scalar_function.function_space()).interpolate(scalar_function + 1)
+    expected = fd.errornorm(scalar_function, g, norm_type=integral_norm_type)
+    got = errornorm(scalar_function, g, norm_type=integral_norm_type)
+    assert np.isclose(expected, got)
+
+
+def test_zero_hdiv(vector_function):
+    """Test that errornorm returns zero for identical vector functions in HDiv norm."""
+    err = errornorm(vector_function, vector_function, norm_type="HDiv")
+    assert np.isclose(err, 0.0)