I just came across some unexpected behavior, when using xr.align with the option join='outer' on two Dataarrays which contain dask.arrays and have different dimension lengths.

MCVE Code Sample

import numpy as np
import xarray as xr

short_time = xr.cftime_range('2000', periods=12)
long_time = xr.cftime_range('2000', periods=120)

data_short = np.random.rand(len(short_time))
data_long = np.random.rand(len(long_time))
a = xr.DataArray(data_short, dims=['time'], coords={'time':short_time}).chunk({'time':3})
b = xr.DataArray(data_long, dims=['time'], coords={'time':long_time}).chunk({'time':3})

a,b = xr.align(a,b, join = 'outer')

Expected Output

As expected a is filled with missing values:

a.plot()
b.plot()

But the filled values do not replicate the chunking along the time dimension in b. Instead the padded values are in one single chunk, which can be substantially larger than the others.

a.data

b.data

(Quick shoutout for the amazing html representation. This made diagnosing this problem super easy! 🥳 )

Problem Description

I think for many problems it would be more appropriate if the padded portion of the array would have a chunking scheme like the longer array.

A practical example (which brought me to this issue) is given in the CMIP6 data archive, where some models give output for several members, with some of them running longer than others, leading to problems when these are combined (see intake-esm/#225).
Basically for that particular model, there are 5 members with a runtime of 100 years and one member with a runtime of 300 years. I think using xr.align leads immediately to a chunk that is 200 years long and blows up the memory on all systems I have tried this on.

Is there a way to work around this, or is this behavior intended and I am missing something?

cc'ing @dcherian @andersy005

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