What happened:

Quite a specific bug! Using map_blocks to wrap a polyfit computation, using a dask client (not the local scheduler) and a time axis with a cftime calendar, I got the error : TypeError: cannot compute the time difference between dates with different calendars.

What you expected to happen:

No bug.

Minimal Complete Verifiable Example:

```python ds = xr.tutorial.open_dataset('rasm').chunk({'x': 25, 'y': 25})

templ = ds.Tair

def func(ds, verbose=False): # Dummy function that call get_clean_interp_index function # Return the Tair as-is just for the test. if verbose: print(ds.time) print(type(ds.time[0].item())) x = xr.core.missing.get_clean_interp_index(ds, 'time')

return ds.Tair

This works (time is a coordinate, so it is already loaded

x = xr.core.missing.get_clean_interp_index(ds, 'time')

This works too. The local scheduler is used.

out = ds.map_blocks( func, template=templ, kwargs={'verbose': False} ) out.load()

This fails!

with Client(n_workers=1, threads_per_worker=8, dashboard_address=8786, memory_limit='7GB') as c: out = ds.map_blocks( func, template=templ, kwargs={'verbose': True} ) out.load() ```

The full traceback is here:

```python --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-12-de89288ffcd5> in <module> 27 kwargs={'verbose': True} 28 ) ---> 29 out.load() /exec/pbourg/.conda/x38/lib/python3.9/site-packages/xarray/core/dataarray.py in load(self, **kwargs) 883 dask.compute 884 """ --> 885 ds = self._to_temp_dataset().load(**kwargs) 886 new = self._from_temp_dataset(ds) 887 self._variable = new._variable /exec/pbourg/.conda/x38/lib/python3.9/site-packages/xarray/core/dataset.py in load(self, **kwargs) 848 849 # evaluate all the dask arrays simultaneously --> 850 evaluated_data = da.compute(*lazy_data.values(), **kwargs) 851 852 for k, data in zip(lazy_data, evaluated_data): /exec/pbourg/.conda/x38/lib/python3.9/site-packages/dask/base.py in compute(*args, **kwargs) 565 postcomputes.append(x.__dask_postcompute__()) 566 --> 567 results = schedule(dsk, keys, **kwargs) 568 return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)]) 569 /exec/pbourg/.conda/x38/lib/python3.9/site-packages/distributed/client.py in get(self, dsk, keys, workers, allow_other_workers, resources, sync, asynchronous, direct, retries, priority, fifo_timeout, actors, **kwargs) 2705 should_rejoin = False 2706 try: -> 2707 results = self.gather(packed, asynchronous=asynchronous, direct=direct) 2708 finally: 2709 for f in futures.values(): /exec/pbourg/.conda/x38/lib/python3.9/site-packages/distributed/client.py in gather(self, futures, errors, direct, asynchronous) 2019 else: 2020 local_worker = None -> 2021 return self.sync( 2022 self._gather, 2023 futures, /exec/pbourg/.conda/x38/lib/python3.9/site-packages/distributed/client.py in sync(self, func, asynchronous, callback_timeout, *args, **kwargs) 860 return future 861 else: --> 862 return sync( 863 self.loop, func, *args, callback_timeout=callback_timeout, **kwargs 864 ) /exec/pbourg/.conda/x38/lib/python3.9/site-packages/distributed/utils.py in sync(loop, func, callback_timeout, *args, **kwargs) 336 if error[0]: 337 typ, exc, tb = error[0] --> 338 raise exc.with_traceback(tb) 339 else: 340 return result[0] /exec/pbourg/.conda/x38/lib/python3.9/site-packages/distributed/utils.py in f() 319 if callback_timeout is not None: 320 future = asyncio.wait_for(future, callback_timeout) --> 321 result[0] = yield future 322 except Exception: 323 error[0] = sys.exc_info() /exec/pbourg/.conda/x38/lib/python3.9/site-packages/tornado/gen.py in run(self) 760 761 try: --> 762 value = future.result() 763 except Exception: 764 exc_info = sys.exc_info() /exec/pbourg/.conda/x38/lib/python3.9/site-packages/distributed/client.py in _gather(self, futures, errors, direct, local_worker) 1884 exc = CancelledError(key) 1885 else: -> 1886 raise exception.with_traceback(traceback) 1887 raise exc 1888 if errors == "skip": /exec/pbourg/.conda/x38/lib/python3.9/site-packages/xarray/core/parallel.py in _wrapper() 284 ] 285 --> 286 result = func(*converted_args, **kwargs) 287 288 # check all dims are present <ipython-input-12-de89288ffcd5> in func() 8 print(ds.time) 9 print(type(ds.time[0].item())) ---> 10 x = xr.core.missing.get_clean_interp_index(ds, 'time') 11 12 return ds.Tair /exec/pbourg/.conda/x38/lib/python3.9/site-packages/xarray/core/missing.py in get_clean_interp_index() 276 index = index.values 277 index = Variable( --> 278 data=datetime_to_numeric(index, offset=offset, datetime_unit="ns"), 279 dims=(dim,), 280 ) /exec/pbourg/.conda/x38/lib/python3.9/site-packages/xarray/core/duck_array_ops.py in datetime_to_numeric() 462 # For np.datetime64, this can silently yield garbage due to overflow. 463 # One option is to enforce 1970-01-01 as the universal offset. --> 464 array = array - offset 465 466 # Scalar is converted to 0d-array src/cftime/_cftime.pyx in cftime._cftime.datetime.__sub__() TypeError: cannot compute the time difference between dates with different calendars ```

The printout to the console. I am calling this in a jupyter notebook so the prints from within workers are in the console, not in the cell's output. I removed useless lines to shorten it.

``` <xarray.DataArray 'time' (time: 36)> array([cftime.datetime(1980, 9, 16, 12, 0, 0, 0, calendar='noleap', has_year_zero=True), cftime.datetime(1980, 10, 17, 0, 0, 0, 0, calendar='noleap', has_year_zero=True), cftime.datetime(1980, 11, 16, 12, 0, 0, 0, calendar='noleap', has_year_zero=True), .... )], dtype=object) Coordinates: * time (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00 Attributes: long_name: time type_preferred: int

<class 'cftime._cftime.datetime'> ```

And for reference: ```python

ds.time array([cftime.DatetimeNoLeap(1980, 9, 16, 12, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(1980, 10, 17, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(1980, 11, 16, 12, 0, 0, 0, has_year_zero=True), ... type(ds.time[0].item()) cftime._cftime.DatetimeNoLeap ``` Anything else we need to know?:

I'm not sure where the exact breaking change lies (dask or cftime?), but this worked with dask 2021.5 and cftime <= 1.4.1. The problem lies in get_clean_interp_index, specifically these lines:

https://github.com/pydata/xarray/blob/5ccb06951cecd59b890c1457e36ee3c2030a67aa/xarray/core/missing.py#L274-L280

On the original dataset, the class of the time values is DatetimeNoLeap whereas the time coordinates received by func are of class datetime, the calendar is only a kwargs. Thus, in get_clean_interp_index the offset is created with the default "standard" calendar and becomes incompatible with the array itself. Which makes datetime_to_numeric fail.

Environment:

Output of <tt>xr.show_versions()</tt>

INSTALLED VERSIONS ------------------ commit: None python: 3.9.5 | packaged by conda-forge | (default, Jun 19 2021, 00:32:32) [GCC 9.3.0] python-bits: 64 OS: Linux OS-release: 3.10.0-514.2.2.el7.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_CA.UTF-8 LOCALE: ('en_CA', 'UTF-8') libhdf5: 1.10.6 libnetcdf: 4.7.4 xarray: 0.18.2 pandas: 1.2.5 numpy: 1.21.0 scipy: 1.7.0 netCDF4: 1.5.6 pydap: None h5netcdf: None h5py: None Nio: None zarr: 2.8.3 cftime: 1.5.0 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2021.06.2 distributed: 2021.06.2 matplotlib: 3.4.2 cartopy: 0.19.0 seaborn: None numbagg: None pint: 0.17 setuptools: 49.6.0.post20210108 pip: 21.1.3 conda: None pytest: None IPython: 7.25.0 sphinx: None