issue_comments: 391114129

This is not really desirable behavior, but it's an implication of how xarray implements ds.resample(time='1M').mean(): - Resample is converted into a groupby call, e.g., ds.groupby(time_starts).mean('time') - .mean('time') for each grouped dataset averages over the 'time' dimension, resulting in a dataset with only a 'space' dimension, e.g., ```

list(ds.resample(time='1M'))[0][1].mean('time') <xarray.Dataset> Dimensions: (space: 10) Coordinates: * space (space) int64 0 1 2 3 4 5 6 7 8 9 Data variables: var_withtime1 (space) float64 0.008982 -0.09879 0.1361 -0.2485 -0.023 ... var_withtime2 (space) float64 0.2621 0.06009 -0.1686 0.07397 0.1095 ... var_timeless1 (space) float64 0.8519 -0.4253 -0.8581 0.9085 -0.4797 ... var_timeless2 (space) float64 0.8006 1.954 -0.5349 0.3317 1.778 -0.7954 ... `` -concat()` is used to combine grouped datasets into the final result, but it doesn't know anything about which variables were aggregated, so every data variable gets the "time" dimension added.

To fix this I would suggest three steps: 1. Add a keep_dims argument to xarray reductions like mean(), indicating that a dimension should be preserved with length 1, like keep_dims=True for numpy reductions (https://github.com/pydata/xarray/issues/2170). 2. Fix concat to only concatenate variables that already have the concatenated dimension, as discussed in https://github.com/pydata/xarray/issues/2064 3. Use keep_dims=True in groupby reductions. Then the result should automatically only include aggregated dimensions. This would convenient allow us to remove existing logic in groupby() for restoring the original order of aggregated dimensions (see _restore_dim_order()).