OK, thanks Joe and Stephan.

On Wed, Aug 30, 2017 at 3:36 PM, Joe Hamman notifications@github.com wrote:

@tbohn https://github.com/tbohn - What is happening here is that xarray is storing the netCDF4 chunk size from the input file. For the LAI variable in your example, that isLAI:_ChunkSizes = 19, 1, 160, 160 ; (you can see this with ncdump -h -s filename.nc).

$ ncdump -s -h veg_hist.0_10n.90_80w.2000_2016.mode_PFT.5dates.nc netcdf veg_hist.0_10n.90_80w.2000_2016.mode_PFT.5dates { dimensions: veg_class = 19 ; lat = 160 ; lon = 160 ; time = UNLIMITED ; // (5 currently) variables: float Cv(veg_class, lat, lon) ; Cv:_FillValue = -1.f ; Cv:units = "-" ; Cv:longname = "Area Fraction" ; Cv:missing_value = -1.f ; Cv:_Storage = "contiguous" ; Cv:_Endianness = "little" ; float LAI(veg_class, time, lat, lon) ; LAI:_FillValue = -1.f ; LAI:units = "m2/m2" ; LAI:longname = "Leaf Area Index" ; LAI:missing_value = -1.f ; LAI:_Storage = "chunked" ; LAI:_ChunkSizes = 19, 1, 160, 160 ; LAI:_Endianness = "little" ; ...

Those integers correspond to the dimensions from LAI. When you slice your dataset, you end up with lat/lon dimensions that are now smaller than the _ChunkSizes. When writing this back to netCDF, xarray is still trying to use the original encoding attribute.

The logical fix is to validate this encoding attribute and either 1) throw an informative error if something isn't going to work, or 2) change the ChunkSizes.

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OK, here's my code and the file that it works (fails) on.

Code: ```Python import os.path import numpy as np import xarray as xr ds = xr.open_dataset('veg_hist.0_10n.90_80w.2000_2016.mode_PFT.5dates.nc') ds_out = ds.isel(lat=slice(0,16),lon=slice(0,16))

ds_out.encoding['unlimited_dims'] = 'time'

ds_out.to_netcdf('test.out.nc') ```

Note that I commented out the attempt to make 'time' unlimited - if I attempt it, I get a slightly different chunk size error ('NetCDF: Bad chunk sizes').

I realize that for now I can use 'ncks' as a workaround, but seems to me that xarray should be able to do this too.

File (attached) veg_hist.0_10n.90_80w.2000_2016.mode_PFT.5dates.nc.zip

(note also that for the example nc file I provided, the slice that my example code makes contains nothing but null values - but that's irrelevant - the error happens for other slices that do contain non-null values.)

I've been encountering this as well, and I don't want to use the scipy engine workaround. If you can tell me what a "self-contained" example means, I can also try to provide one.