html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/521#issuecomment-474898722,https://api.github.com/repos/pydata/xarray/issues/521,474898722,MDEyOklzc3VlQ29tbWVudDQ3NDg5ODcyMg==,1197350,2019-03-20T15:55:15Z,2019-03-20T15:55:15Z,MEMBER,"@klindsay28 -- thanks for the clarification. You're clearly right about 2, and I was misinformed. The problem is that 3 makes it impossible follow the CF convention rules to overcome 2 (which xarray would try to do).","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,99836561
https://github.com/pydata/xarray/issues/521#issuecomment-474840796,https://api.github.com/repos/pydata/xarray/issues/521,474840796,MDEyOklzc3VlQ29tbWVudDQ3NDg0MDc5Ng==,1197350,2019-03-20T13:58:09Z,2019-03-20T13:58:46Z,MEMBER,"It's important to be clear that the issues 2 and 3 that @spencerkclark pointed out are objectively **errors in the metadata**. We have worked very hard over many years to enable xarray to correctly parse CF-compliant dates with non-standard calendars. But xarray cannot and should not be expected to magically fix metadata that is inconsistent or incomplete. 

You really need to bring these issues to the attention of whoever generated `some_CESM_output_file.nc`.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,99836561
https://github.com/pydata/xarray/issues/521#issuecomment-129189753,https://api.github.com/repos/pydata/xarray/issues/521,129189753,MDEyOklzc3VlQ29tbWVudDEyOTE4OTc1Mw==,1197350,2015-08-09T14:00:37Z,2015-08-09T14:00:37Z,MEMBER,"@jhamman Thanks for the clear explanation! One of the main uses for non-standard calendars would be climate model ""control runs"", which don't occur any any specific point in historical time but still have seasonal cycles, well defined months, etc. It would be nice to have ""group by"" functionality for these datasets. But I do see how this is impossible with the current numpy datetime64 datatype. Perhaps the long term fix is to implement non-standard calendars within numpy itself.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,99836561
https://github.com/pydata/xarray/issues/521#issuecomment-129069915,https://api.github.com/repos/pydata/xarray/issues/521,129069915,MDEyOklzc3VlQ29tbWVudDEyOTA2OTkxNQ==,1197350,2015-08-08T23:30:38Z,2015-08-08T23:30:38Z,MEMBER,"The PR above fixes this issue. However, since my model years are in the range 100-200, I am still getting the warning 

```
RuntimeWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy netCDF4.datetime objects instead, reason: dates out of range
```

and eventually when I try to access the time data, an error with a very long stack trace ending with

```
pandas/tslib.pyx in pandas.tslib.Timestamp.__new__ (pandas/tslib.c:7638)()
pandas/tslib.pyx in pandas.tslib.convert_to_tsobject (pandas/tslib.c:21232)()
pandas/tslib.pyx in pandas.tslib._check_dts_bounds (pandas/tslib.c:23332)()
OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 100-02-01 00:00:00
```

I see there is a check in conventions.py that the year has to lie between 1678 and 2226. What is the reason for this?
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,99836561
https://github.com/pydata/xarray/issues/521#issuecomment-129054598,https://api.github.com/repos/pydata/xarray/issues/521,129054598,MDEyOklzc3VlQ29tbWVudDEyOTA1NDU5OA==,1197350,2015-08-08T21:59:54Z,2015-08-08T21:59:54Z,MEMBER,"In fact I just found a netCDF issue on this topic! Apparently they don't think it should be supported. Unidata/netcdf4-python#442
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,99836561