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Sea Surface Temperature Daily Analysis: European Space Agency Climate Change Initiative product version 2.1

Data Summary

Global daily-mean sea surface temperatures, presented on a 0.05° latitude-longitude grid, with gaps between available daily observations filled by statistical means, spanning late 1981 to recent time.

Synthesised from multiple Earth orbiting satellites carrying infrared imagers from thousands of billions of individual measurements. Underlying observation resolution ranges from 1 to 20 km, and after gap filling the typical feature resolution is ~20 km. Suitable for large-scale oceanographic meteorological and climatological applications, such as evaluating or constraining environmental models or case-studies of marine heat wave events.

Adhering to community data standards and names. Includes temperature uncertainty information and auxiliary information about land-sea fraction and sea-ice coverage. To understand the data for your application, read the paper [1] using www.nature.com/articles/s41597-019-0236-x to cite in any published usage.

The v2.1 record is known to have biases associated with desert dust aerosol and erratic calibration of early-record sensors [1]. Adjustments to reduce these biases and include additional uncertainty in these effects have been developed, as described in [2] and are applied to this data. These adjustments operate on monthly and >5 degree time-space scales.

This data is available on the Registry of Open Data on AWS.

Data structure

Accessing the data

        import xarray as xr
        import matplotlib.pyplot as plt
        import s3fs
        s3 = s3fs.S3FileSystem(anon=True)
        store = s3fs.S3Map(root="s3://surftemp-sst/data/sst.zarr", s3=s3, create=False)
        sst_ds = xr.open_zarr(store)
        # select an area covering the great barrier reef at a particular date
        da = sst_ds["analysed_sst"].sel(time="2016-02-18",
                lat=slice(-15.6,-10.69867),lon=slice(141.4,153.6))
        da.plot(cmap="coolwarm")
        plt.show() # need this if not running in a jupyter notebook
    

Tutorials

Tutorial 1: Getting Started

Tutorial 2: Worked Example - Marine Heatwaves

Running the tutorial notebooks locally

If downloading the tutorial notebooks, follow these instructions to setup a conda environment with the correct python dependencies.

Related Resources

• A free-to-use service for obtaining regridded, timeseries or regional versions of this data is available at surftemp.net
• This data applies a bias adjustment[2] to the dataset held at the Copernicus Climate Data Store: Sea surface temperature daily data from 1981 to present derived from satellite observations

Problems with or questions about these tutorials?

References

[1] Merchant, C.J., Embury, O., Bulgin, C.E., Block, T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R. and Donlon, C. (2019), Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific Data 6, 223, doi:10.1038/s41597-019-0236-x

[2] Merchant, C.J. and Embury, O. (2020) Adjusting for desert-dust-related biases in a climate data record of sea surface temperature. Remote Sensing, 12 (16). 2554. ISSN 2072-4292 doi:10.3390/rs12162554

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