I got a similar error message when opening a Zarr store with datetime64 time values, where I tried to set the "calendar" attribute on the time axis (the attribute was unset in the original store). I've found some xarray code that appears to treat the "calendar" and "units" time attributes as special, and it essentially prevents users from setting or changing these values, even when those values are not present:

From xarray/coding/times.py:

``` class CFDatetimeCoder(VariableCoder): def init(self, use_cftime=None): self.use_cftime = use_cftime

def encode(self, variable, name=None):
    dims, data, attrs, encoding = unpack_for_encoding(variable)
    if np.issubdtype(data.dtype, np.datetime64) or contains_cftime_datetimes(
        variable
    ):
        (data, units, calendar) = encode_cf_datetime(
            data, encoding.pop("units", None), encoding.pop("calendar", None)
        )
        safe_setitem(attrs, "units", units, name=name)
        safe_setitem(attrs, "calendar", calendar, name=name)

    return Variable(dims, data, attrs, encoding)

```

@mathause

thanks for your suggestions. Your first solution works fine for correcting the time data stored in the array. I also don't understand why ds_test.time.encoding is empty and yet, its the reason for an error while saving it. Maybe its a bug?

@Chan-Jer Another work around which I used was to set the correct time value using cdo's settime function.

The following works (using assign_coords() instead of time.values=). I think you also forgot a str in date_range.

```python import numpy as np import xarray as xr import pandas as pd

files = ['ds_1.nc', 'ds_2.nc']

ds_test = xr.open_mfdataset(files, combine='nested', concat_dim='time', decode_cf=False) yr = 1988 # year dates = pd.date_range(start=str(yr), end=str(yr+1), freq='6H', closed='left') ds_test.assign_coords(time=dates[:2]) ds_test.time.encoding['units'] = "seconds since 1970-01-01 00:00:00" ds_test.time.encoding['calendar'] = "proleptic_gregorian" ds_test.to_netcdf('ds_1_2.nc')

```

The following works as well: python ds_test = xr.open_mfdataset(files, combine='nested', concat_dim='time') ds_test.time.encoding['units'] = "seconds since 1970-01-01 00:00:00" ds_test.to_netcdf('ds_1_2.nc')

However, what does indeed not work is the following

python ds_test = xr.open_mfdataset(files, combine='nested', concat_dim='time') ds_test.time.attrs['units'] = "seconds since 1970-01-01 00:00:00" ds_test.to_netcdf('ds_1_2.nc') which I don't entirely understand, because ds_test.time.encoding is empty. So maybe there is an encoding hidden somewhere, but I couldn't find it.

hey,avatar, thanks for your examples. I'm wondering that problem has figured out or not . I have met the same problem. I'm appreciated for any help.

Hi Ryan, thanks for your reply. Apologies for not creating a reproducible problem earlier as the files weren't created by xarray routine. Please find my attempt at reproducing the problem below:

Minimum steps to reproduce the error

```python import numpy as np import xarray as xr import pandas as pd

data1 = np.ones(shape=(1, 181, 360)) lats=np.arange(-90,91, 1) lons=np.arange(-180,180,1) time1 = np.array([0])

creating the first dataset

da_1 = xr.DataArray(data1, coords=[time1, lats, lons], dims=['time', 'lats', 'lons']) da_1.time.attrs['units'] = "hours since 1988-01-01 00:00:00" da_1.time.attrs['calendar'] = "proleptic_gregorian" da_1.time.attrs['standard_name'] = "time" ds_1 = xr.Dataset({'V':da_1}) ds_1.attrs['Conventions'] = 'CF' ds_1.to_netcdf('ds_1.nc', encoding=None)

creating second test dataset

time2=np.array([6]) # wrong time value da_2 = xr.DataArray(data1, coords=[time2, lats, lons], dims=['time', 'lats', 'lons']) da_2.time.attrs['units'] = "hours since 1988-01-01 06:00:00" da_2.time.attrs['calendar'] = "proleptic_gregorian" da_2.time.attrs['standard_name'] = "time"

ds_2 = xr.Dataset({'V':da_2}) ds_2.attrs['Conventions'] = 'CF'

saving it with wrong time value

ds_2.to_netcdf('ds_2.nc', encoding=None)

Reading the 2 files and concatenating them

files = ['/path/to/ds_1.nc', '/path/to/ds_2.nc']

ds_test = xr.open_mfdataset(files, combine='nested', concat_dim='time', decode_cf=False) yr = 1988 # year dates = pd.date_range(start=(yr), end=str(yr+1), freq='6H', closed='left') ds_test.time.values=dates[:2] # fixing the time values ds_test.time.attrs['units'] = "Seconds since 1970-01-01 00:00:00" #required encoding ds_test.to_netcdf('ds_1_2.nc') # gives the same error ```

ValueError: failed to prevent overwriting existing key units in attrs on variable 'time'. This is probably an encoding field used by xarray to describe how a variable is serialized. To proceed, remove this key from the variable's attributes manually.

I've also mentioned your suggestion earlier in the original post. It also gives the same error message Please find the following reproducible steps incorporating your suggestion.

Trying time encoding solution

```

Reading the files

files = ['/path/to/ds_1.nc', '/path/to/ds_2.nc']

ds_test = xr.open_mfdataset(files, combine='nested', concat_dim='time', decode_cf=False) yr = 1988 # year dates = pd.date_range(start=(yr), end=str(yr+1), freq='6H', closed='left') ds_test.time.values=dates[:2] # fixing the time values

encoding try

ds_test.time.encoding['units'] = "Seconds since 1970-01-01 00:00:00" ds_test.to_netcdf('ds_1_2.nc') # gives same error ```

ValueError: failed to prevent overwriting existing key calendar in attrs on variable 'time'. This is probably an encoding field used by xarray to describe how a variable is serialized. To proceed, remove this key from the variable's attributes manually.

Hi @avatar101 - thanks for your issue!

I couldn't fully reproduce your example, since it references files that I don't have access to. For the future, please consider trying to create a "Minimal, Complete and Verifiable Example" (MCVE): (http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports)

In the meantime, I do have a suggestion you could try. Instead of python ds_test.time.attrs['units'] = "Seconds since 1970-01-01 00:00:00" try python ds_test.time.encoding['units'] = "Seconds since 1970-01-01 00:00:00"

The reason is that, since you have created your time coordinate using pd.date_range, which returns a datetime64 dtype, that variable is already considered to be encoded.