What happened?

When opening MUR SST netcdfs from S3, xarray.open_dataset(file, engine="h5netcdf", chunks={}) returns a single chunk (whereas the h5netcdf library returns a chunk shape of (1, 1023, 2047).

A notebook version of the code below includes the output: https://gist.github.com/abarciauskas-bgse/9366e04d2af09b79c9de466f6c1d3b90

What did you expect to happen?

I thought the chunks={} option would return the same chunks (1, 1023, 2047) exposed by the h5netcdf engine.

Minimal Complete Verifiable Example

```Python

!/usr/bin/env python

coding: utf-8

This notebook looks at how xarray and h5netcdf return different chunks.

import pandas as pd import h5netcdf import s3fs import xarray as xr

dates = [ d.to_pydatetime().strftime('%Y%m%d') for d in pd.date_range('2023-02-01', '2023-03-01', freq='D') ]

SHORT_NAME = 'MUR-JPL-L4-GLOB-v4.1' s3_fs = s3fs.S3FileSystem(anon=False) var = 'analysed_sst'

def make_filename(time): base_url = f's3://podaac-ops-cumulus-protected/{SHORT_NAME}/' # example file: "/20020601090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc" return f'{base_url}{time}090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc'

s3_urls = [make_filename(d) for d in dates]

def print_chunk_shape(s3_url): try: # Open the dataset using xarray file = s3_fs.open(s3_url) dataset = xr.open_dataset(file, engine='h5netcdf', chunks={})

    # Print chunk shapes for each variable in the dataset
    print(f"\nChunk shapes for {s3_url}:")
    if dataset[var].chunks is not None:
        print(f"xarray open_dataset chunks for {var}: {dataset[var].chunks}")
    else:
        print(f"xarray open_dataset chunks for {var}: Not chunked")

    with h5netcdf.File(file, 'r') as file:
        dataset = file[var]

        # Check if the dataset is chunked
        if dataset.chunks:
            print(f"h5netcdf chunks for {var}:", dataset.chunks)
        else:
            print(f"h5netcdf dataset is not chunked.")

except Exception as e:
    print(f"Failed to process {s3_url}: {e}")

[print_chunk_shape(s3_url) for s3_url in s3_urls] ```

MVCE confirmation

• [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [x] Complete example — the example is self-contained, including all data and the text of any traceback.
• [x] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• [x] New issue — a search of GitHub Issues suggests this is not a duplicate.
• [x] Recent environment — the issue occurs with the latest version of xarray and its dependencies.

Relevant log output

No response

Anything else we need to know?

No response

Environment

INSTALLED VERSIONS ------------------ commit: None python: 3.10.12 | packaged by conda-forge | (main, Jun 23 2023, 22:40:32) [GCC 12.3.0] python-bits: 64 OS: Linux OS-release: 5.10.198-187.748.amzn2.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: C.UTF-8 LANG: C.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.14.1 libnetcdf: 4.9.2 xarray: 2023.6.0 pandas: 2.0.3 numpy: 1.24.4 scipy: 1.11.1 netCDF4: 1.6.4 pydap: installed h5netcdf: 1.2.0 h5py: 3.9.0 Nio: None zarr: 2.15.0 cftime: 1.6.2 nc_time_axis: 1.4.1 PseudoNetCDF: None iris: None bottleneck: 1.3.7 dask: 2023.6.1 distributed: 2023.6.1 matplotlib: 3.7.1 cartopy: 0.21.1 seaborn: 0.12.2 numbagg: None fsspec: 2023.6.0 cupy: None pint: 0.22 sparse: 0.14.0 flox: 0.7.2 numpy_groupies: 0.9.22 setuptools: 68.0.0 pip: 23.1.2 conda: None pytest: 7.4.0 mypy: None IPython: 8.14.0 sphinx: None