This will allow xray users to take advantage of dask's nascent support for
caching intermediate results (dask/dask#502).

For example:

	In [1]: import xray

	In [2]: from dask.diagnostics.cache import Cache

	In [3]: c = Cache(5e7)

	In [4]: c.register()

	In [5]: ds = xray.open_mfdataset('/Users/shoyer/data/era-interim/2t/2014-*.nc', engine='scipy')

	In [6]: %time ds.sum().load()
	CPU times: user 2.72 s, sys: 2.7 s, total: 5.41 s
	Wall time: 3.85 s
	Out[6]:
	<xray.Dataset>
	Dimensions:  ()
	Coordinates:
	    *empty*
	Data variables:
	    t2m      float64 5.338e+10

	In [7]: %time ds.mean().load()
	CPU times: user 5.31 s, sys: 1.86 s, total: 7.17 s
	Wall time: 1.81 s
	Out[7]:
	<xray.Dataset>
	Dimensions:  ()
	Coordinates:
	    *empty*
	Data variables:
	    t2m      float64 279.0

	In [8]: %time ds.mean().load()
	CPU times: user 7.73 ms, sys: 2.73 ms, total: 10.5 ms
	Wall time: 8.45 ms
	Out[8]:
	<xray.Dataset>
	Dimensions:  ()
	Coordinates:
	    *empty*
	Data variables:
	    t2m      float64 279.0