Suggestion the default of chunks kwarg from 'auto' to {} or None

**Is your feature request related to a problem? Please describe.**
I’ve noticed a error when upgrading from Xarray 2025.9.1 to 2026.2.0. In the older version, chunks='auto' handled these datasets without issue. However, the newer version now triggers a NotImplementedError when encountering object dtypes. This suggests that the internal size-estimation logic in recent Xarray/Dask updates is now strictly enforcing a check that was previously bypassed
```
Failed to load dataset with key='bvf2-theta-an-gauss.bvf2-theta-an-gauss'
You can use `cat['bvf2-theta-an-gauss.bvf2-theta-an-gauss'].df` to inspect the assets/files for this key.

File "//conda-envs//lib/python3.14/site-packages/intake_esm/source.py", line 292, in _open_dataset datasets = dask.compute(*datasets)
File "//conda-envs//lib/python3.14/site-packages/dask/base.py", line 685, in compute results = schedule(expr, keys, **kwargs)
File "//conda-envs//lib/python3.14/site-packages/intake_esm/source.py", line 67, in _delayed_open_ds return _open_dataset(*args, **kwargs)
File "//conda-envs//lib/python3.14/site-packages/intake_esm/source.py", line 109, in _open_dataset ds = xr.open_dataset(url, **xarray_open_kwargs)
File "//conda-envs//lib/python3.14/site-packages/xarray/backends/api.py", line 613, in open_dataset backend_ds, ^^^^^^^^^^
...<13 lines>... chunked_array_type,
File "//conda-envs//lib/python3.14/site-packages/xarray/backends/api.py", line 308, in _dataset_from_backend_dataset ds, ^^
...<9 lines>... inline_array,
File "//conda-envs//lib/python3.14/site-packages/xarray/backends/api.py", line 251, in _chunk_ds var._data, ^
...<5 lines>... preferred_chunks=var.encoding.get("preferred_chunks", {}),
File "//conda-envs//lib/python3.14/site-packages/xarray/namedarray/utils.py", line 239, in _get_chunk chunk_shape, ^^
...<5 lines>... limit=limit,
File "//conda-envs//lib/python3.14/site-packages/xarray/namedarray/daskmanager.py", line 57, in normalize_chunks chunks, ^^^^
...<5 lines>... dtype=dtype,
File "//conda-envs//lib/python3.14/site-packages/dask/array/core.py", line 3302, in auto_chunks "Can not use auto rechunking with object dtype. "
...<2 lines>...
NotImplementedError: Can not use auto rechunking with object dtype. We are unable to estimate the size in bytes of object data
```

**Describe the solution you'd like**
This error generally doesn't occur in standard xarray.open_dataset workflows because the default for chunks is None. However, since intake-esm defaults to chunks='auto', it forces a re-evaluation of chunk sizes that fails on object dtypes. I suggest changing the default chunks kwarg to either None (to align with xarray's default) or {} (to enable Dask while respecting the dataset's native on-disk structure).

**Describe alternatives you've considered**
I suggest changing the default for the chunks kwarg from 'auto' to None. This would align more closely with user expectations; if no xarray_open_kwargs are provided, most users assume xarray's native defaults apply. Alternatively, if a Dask-compatible default is preferred, using chunks={} would be a safer choice, as it preserves the original on-disk chunking and avoids the NotImplementedError currently triggered by object dtypes during 'auto' rechunking.

**Additional context**
I’m happy to open a PR to address this. I'd appreciate your thoughts on the best approach, as well as any insight into why auto chunking is being triggered for these specific dtypes. Thanks! I will also make a xarray related issue related to this issue.

The following code will reproduce the error above

```
import intake
catalog_url = "https://data.gdex.ucar.edu/d640000/catalogs/d640000-osdf.json"
cat = intake.open_esm_datastore(catalog_url)
cat_analysis = cat.search(variable='bvf2-theta-an-gauss')
dict_datasets = cat_analysis.to_dataset_dict(xarray_open_kwargs={'engine':'kerchunk'})
```

the conda env yml
```
name: arco_test
channels:
- conda-forge
- defaults
dependencies:
- python>3.11
- pip
# Core Data & Geospatial
- xarray == 2026.2.0
- netcdf4
- zarr
- fastparquet
- kerchunk
- dask-jobqueue
- intake-esm >=2025.12.12
# Visualization & Utilities
- matplotlib
- jupyterlab
- pip:
- pelicanfs>=1.3.1
```