issue_comments: 357996887

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https://github.com/pydata/xarray/issues/1832#issuecomment-357996887 https://api.github.com/repos/pydata/xarray/issues/1832 357996887 MDEyOklzc3VlQ29tbWVudDM1Nzk5Njg4Nw== 306380 2018-01-16T15:27:33Z 2018-01-16T15:27:33Z MEMBER

```python

monthly climatology

ds_mm = ds.groupby('time.month').mean(dim='time')

anomaly

ds_anom = ds.groupby('time.month')- ds_mm ```

I would actually hope that this would be a little bit nicer than the case in the dask issue, especially if you are chunked by some dimension other than time. In the case that @shoyer points to we're creating a global aggregation value and then applying that to all input data. In @rabernat's case we have at least twelve aggregation points and possibly more if there are other chunked dimensions like ensemble (or lat/lon if you choose to chunk those).

 
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