issues: 281983819

Code Sample, a copy-pastable example if possible

python import numpy as np import xarray as xr xr.DataArray(np.random.rand(1000)).to_dataset(name='random').chunk(100) ds_cf = xr.decode_cf(ds) assert not ds_cf.chunks

Problem description

Calling decode_cf causes variables whose data is dask arrays to be wrapped in two layers of abstractions: DaskIndexingAdapter and LazilyIndexedArray. In the example above ```python

ds.random.variable._data dask.array<da.random.random_sample, shape=(1000,), dtype=float64, chunksize=(100,)> ds_cf.random.variable._data LazilyIndexedArray(array=DaskIndexingAdapter(array=dask.array<da.random.random_sample, shape=(1000,), dtype=float64, chunksize=(100,)>), key=BasicIndexer((slice(None, None, None),))) ``` At least part of the problem comes from this line: https://github.com/pydata/xarray/blob/master/xarray/conventions.py#L1045

This is especially problematic if we want to concatenate several such datasets together with dask. Chunking the decoded dataset creates a nested dask-within-dask array which is sure to cause undesirable behavior down the line

```python

dict(ds_cf.chunk().random.data.dask) {('xarray-random-bf5298b8790e93c1564b5dca9e04399e', 0): (<function dask.array.core.getter>, 'xarray-random-bf5298b8790e93c1564b5dca9e04399e', (slice(0, 1000, None),)), 'xarray-random-bf5298b8790e93c1564b5dca9e04399e': ImplicitToExplicitIndexingAdapter(array=LazilyIndexedArray(array=DaskIndexingAdapter(array=dask.array<da.random.random_sample, shape=(1000,), dtype=float64, chunksize=(100,)>), key=BasicIndexer((slice(None, None, None),))))} ```

Expected Output

If we call decode_cf on a dataset made of dask arrays, it should preserve the chunks of the original dask arrays. Hopefully this can be addressed by #1752.

Output of xr.show_versions()