Today @lukasbrunner and me ran into this problem. Opening an mfdataset and saving to_netcdfled to the following units for time and time_units:

time_bnds:units = "days since 1850-01-01" ;
time:units = "days since 2000-01-01" ;

Opening the dataset in xarray works fine, but ncdump and ncview use the units from time for time_bnds. Thus if I do

ncdump -tv time_bnds test.nc

the first date is '2150-01-01' (instead of '2000-01-01') - very confusing. (panoply shows the correct time_bnds).

Workaround

``` python import xarray as xr filenames = ['file1.nc', 'file2.nc']

ds = xr.open_mfdataset(fNs) ds.load()

make sure the encoding is really empty

assert not ds.time.encoding

assign encoding, such that they are equal

ds.time.encoding.update(ds.time_bnds.encoding)

save

ds.to_netcdf('~/F/test.nc')

```

Btw. thanks to @klindsay28 for the nice error report.

We already have special treatment for time_bounds in the decode step. It makes sense to treat it specially in the encode step.

+1 I didn't fully appreciate how strict the CF conventions were regarding this at the time of #2571. Where it is unambiguous I agree we should make an effort to comply (or preserve compliance).

Thanks for the great report @klindsay28.

Looks like CF recommends that time_bounds (boundary variable) not have the units or calendar attributes:

Since a boundary variable is considered to be part of a coordinate variable’s metadata, it is not necessary to provide it with attributes such as long_name and units.

Boundary variable attributes which determine the coordinate type (units, standard_name, axis and positive) or those which affect the interpretation of the array values (units, calendar, leap_month, leap_year and month_lengths) must always agree exactly with the same attributes of its associated coordinate, scalar coordinate or auxiliary coordinate variable. To avoid duplication, however, it is recommended that these are not provided to a boundary variable.

We already have special treatment for time_bounds in the decode step. It makes sense to treat it specially in the encode step. In fact it looks like @spencerkclark identified this issue in https://github.com/pydata/xarray/pull/2571 :clap:

@klindsay28 Any interest in putting together a PR that would avoid setting these attributes on time_bounds during the encode step?

Ping @fmaussion for feedback.

It looks like ds.time.encoding is not getting set when open_mfdataset is opening multiple files. I suspect that this is leading to the surprising unit for time when the ds is written out. The code below demonstrates that ds.time.encoding is set by open_mfdataset in the single-file case and is not set in the multi-file case. However, ds.time_bounds.encoding is set in both the single- and multi-file cases.

The possibility of this is alluded to the in a comment in #2436, which relates the issue to #1614.

``` import numpy as np import xarray as xr

create time and time_bounds DataArrays for Jan-1850 and Feb-1850

time_bounds_vals = np.array([[0.0, 31.0], [31.0, 59.0]]) time_vals = time_bounds_vals.mean(axis=1)

time_var = xr.DataArray(time_vals, dims='time', coords={'time':time_vals}) time_bounds_var = xr.DataArray(time_bounds_vals, dims=('time', 'd2'), coords={'time':time_vals})

create Dataset of time and time_bounds

ds = xr.Dataset(coords={'time':time_var}, data_vars={'time_bounds':time_bounds_var}) ds.time.attrs = {'bounds':'time_bounds', 'calendar':'noleap', 'units':'days since 1850-01-01'}

write Jan-1850 values to file

ds.isel(time=slice(0,1)).to_netcdf('Jan-1850.nc', unlimited_dims='time')

write Feb-1850 values to file

ds.isel(time=slice(1,2)).to_netcdf('Feb-1850.nc', unlimited_dims='time')

use open_mfdataset to read in files, combining into 1 Dataset

decode_times = True decode_cf = True ds = xr.open_mfdataset(['Jan-1850.nc'], decode_cf=decode_cf, decode_times=decode_times)

print('time and time_bounds encoding, single-file open_mfdataset') print(ds.time.encoding) print(ds.time_bounds.encoding)

use open_mfdataset to read in files, combining into 1 Dataset

decode_times = True decode_cf = True ds = xr.open_mfdataset(['Jan-1850.nc', 'Feb-1850.nc'], decode_cf=decode_cf, decode_times=decode_times)

print('--------------------') print('time and time_bounds encoding, multi-file open_mfdataset') print(ds.time.encoding) print(ds.time_bounds.encoding) ```

produces

``` time and time_bounds encoding, single-file open_mfdataset {'zlib': False, 'shuffle': False, 'complevel': 0, 'fletcher32': False, 'contiguous': False, 'chunksizes': (512,), 'source': '/gpfs/fs1/work/klindsay/analysis/CESM2_coup_carb_cycle_JAMES/Jan-1850.nc', 'original_shape': (1,), 'dtype': dtype('float64'), '_FillValue': nan, 'units': 'days since 1850-01-01', 'calendar': 'noleap'} {'zlib': False, 'shuffle': False, 'complevel': 0, 'fletcher32': False, 'contiguous': False, 'chunksizes': (1, 2), 'source': '/gpfs/fs1/work/klindsay/analysis/CESM2_coup_carb_cycle_JAMES/Jan-1850.nc', 'original_shape': (1, 2), 'dtype': dtype('float64'), '_FillValue': nan, 'units': 'days since 1850-01-01', 'calendar': 'noleap'}

time and time_bounds encoding, multi-file open_mfdataset {} {'zlib': False, 'shuffle': False, 'complevel': 0, 'fletcher32': False, 'contiguous': False, 'chunksizes': (1, 2), 'source': '/gpfs/fs1/work/klindsay/analysis/CESM2_coup_carb_cycle_JAMES/Jan-1850.nc', 'original_shape': (1, 2), 'dtype': dtype('float64'), '_FillValue': nan, 'units': 'days since 1850-01-01', 'calendar': 'noleap'} ```