Maintain the type of scalars when merging.

[pelson]

The type of a scalar coordinate is not preserved for scalars:

>>> ml_10 = iris.Constraint(model_level_number=10)
>>> print type(iris.load_strict('/data/local/dataZoo/iris_resources/tests/PP/globClim1/dec_subset.pp', \
...                                                  'air_potential_temperature' & ml_10).coord('sigma'))
<class 'iris.coords.DimCoord'>

>>> print type(iris.load_strict('/data/local/dataZoo/iris_resources/tests/PP/globClim1/dec_subset.pp',  \
...                                                 'air_potential_temperature').extract(ml_10).coord('sigma'))
<class 'iris.coords.AuxCoord'>

[cpelley]

This is not an issue with merging. Sigma is not a monotonic coordinate, hence why it is an AuxCoord if we load then extract. Providing two constraints on loading allows it to be deemed monotonic.

For example (coord1 here is not monotonic):
coord1 = [1, 3, 2, 2, 3, 1]
coord2 = [1, 2, 3, 4, 5, 6]

Constrained load based on coord2 values (0, 2, 4) results in a monotonic coord1 coordinate:
coord1 = [1, 2, 3]
coord2 = [1, 3, 5]

So I think the real bug here is that we cannot infer that coord1 is now a dimension coordinate (monotonic) just because we have extracted values that happen to make it monotonic (and therefore a dimension coordinate).