issue_comments

Hi Matthias, I think your solution is fine. The best is simply to avoid "months" as units altogether.

If one has a "real" calendar one can also let pandas and xarray do the job:

python t = pd.date_range(start='1980-01', end='2010-12', freq='MS') target = np.random.rand(len(t), 10, 10) lat = np.arange(50, 51, 0.1) lon = np.arange(3, 4, 0.1) target_xr = xr.Dataset({'test': (['time', 'lat', 'lon'], target)}, coords={'time': ('time', t), 'lat': lat, 'lon': lon} ) target_xr.to_netcdf('test_2.nc') which creates the following time units automatically:

int64 time(time) ; time:units = "days since 1980-01-01 00:00:00" ; time:calendar = "proleptic_gregorian" ;

I think I do mean 'years' in the CF convention sense

Can you pinpoint to which part of the CF convention? From the link I read: a year is exactly 365.242198781 days, which would lead to highly unlikely calendar dates.

I agree however that interpreting "years" as being "calendar years" is the only way that makes sense.

For the record, netCDF4 also doesn't like "years":

```python import netCDF4 ds = netCDF4.Dataset('/home/mowglie/Downloads/histsoc_population_0.5deg_1861-2005.nc4') time = ds.variables['time'] netCDF4.num2date(time[:], units=time.units)

ValueError Traceback (most recent call last) <ipython-input-15-b38f64c7bce4> in <module>() 2 ds = netCDF4.Dataset('histsoc_population_0.5deg_1861-2005.nc4') 3 time = ds.variables['time'] ----> 4 netCDF4.num2date(time[:], units=time.units)

netCDF4/_netCDF4.pyx in netCDF4._netCDF4.num2date (netCDF4/_netCDF4.c:66463)()

ValueError: unsupported time units ```

I am not sure to understand what you are asking us to do here. The problem with "years" is that their use is not recommended by the CF conventions.

Very often (and I think your file means it this way), users would like years to be simple "calendar years" , i.e. : 1901-01-01, 1902-01-01, but this is not what the unit "years" means in the CF conventions: see http://cfconventions.org/cf-conventions/v1.6.0/cf-conventions.html#time-coordinate