Data Access Form

Dataset Title:	MHW Raw / 1987_2016_Climatology / 90p / climatology
Institution:	Danish Meteorological Institute (Dataset ID: mhw_raw_1987_2016_climatology_90p_clim)
Information:	Summary \| License \| FGDC \| ISO 19115 \| Metadata \| Background \| Files \| Make a graph

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Dimensions	Size	Spacing
dayofyear (1)	366	1.0 (even)

latitude (degrees_north)	3600	0.05 (uneven)

longitude (degrees_east)	7200	0.05 (uneven)

Grid Variables (which always also download all of the dimension variables)

mean (Climatological daily mean of analysed_sst, degree_K)

p10 (Climatological daily 10th percentile of analysed_sst, degree_K)

p90 (Climatological daily 90th percentile of analysed_sst, degree_K)

threshCS (Marine cold-spell threshold anomaly (p10 minus climatological mean), degree_K)

threshHW (Marine heatwave threshold anomaly (p90 minus climatological mean), degree_K)

File type: (more information)

(Documentation / Bypass this form)

(Please be patient. It may take a while to get the data.)

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
  dayofyear {
    Int32 actual_range 1, 366;
    String ioos_category "Time";
    String long_name "Day of year";
    String units "1";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 actual_range -89.9749984741211, 89.9749984741211;
    String axis "Y";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 actual_range -179.97500610351562, 179.97500610351562;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  mean {
    UInt32 _ChunkSizes 1, 1, 7200;
    Float64 _FillValue NaN;
    String ancillary_variables "analysed_sst_uncertainty mask";
    String cell_methods "time: mean within days time: mean over years";
    String depth "20 cm";
    String description "Daily climatological mean of analysed_sst over the reference period 1987-2016. Two-pass smoothing applied: 11-day rolling mean (equivalent to Hobday et al. (2016) +/-5-day cross-DOY pooling) followed by a 31-day rolling mean.";
    String ioos_category "Temperature";
    String long_name "Climatological daily mean of analysed_sst";
    Float64 missing_value NaN;
    String reference_period "1987-2016";
    String source "ATSR<1,2>-ESACCI-L3U-v3.0, AATSR-ESACCI-L3U-v3.0, SLSTR<A,B>-ESACCI-L3U-ICDR-v3.0 AVHRR<06,07,08,09,10,11,12,14,15,16,17,18,19>_G-ESACCI-L3U-v3.0, AVHRRMT<A,B>-ESACCI-L3U-v3.0, AMSR<E,2>-ESACCI-L2P-v2.0";
    String standard_name "sea_water_temperature";
    String units "degree_K";
  }
  p10 {
    UInt32 _ChunkSizes 1, 1, 7200;
    Float64 _FillValue NaN;
    String ancillary_variables "analysed_sst_uncertainty mask";
    String cell_methods "time: percentile (value=10) within days time: percentile (value=10) over years";
    String depth "20 cm";
    String description "Daily 10th percentile of analysed_sst over the reference period 1987-2016, used as the cold-spell threshold base following Hobday et al. (2016). Two-pass smoothing applied: 11-day rolling mean (equivalent to Hobday et al. (2016) +/-5-day cross-DOY pooling) followed by a 31-day rolling mean.";
    String ioos_category "Temperature";
    String long_name "Climatological daily 10th percentile of analysed_sst";
    Float64 missing_value NaN;
    Int32 percentile 10;
    String reference_period "1987-2016";
    String source "ATSR<1,2>-ESACCI-L3U-v3.0, AATSR-ESACCI-L3U-v3.0, SLSTR<A,B>-ESACCI-L3U-ICDR-v3.0 AVHRR<06,07,08,09,10,11,12,14,15,16,17,18,19>_G-ESACCI-L3U-v3.0, AVHRRMT<A,B>-ESACCI-L3U-v3.0, AMSR<E,2>-ESACCI-L2P-v2.0";
    String standard_name "sea_water_temperature";
    String units "degree_K";
  }
  p90 {
    UInt32 _ChunkSizes 1, 1, 7200;
    Float64 _FillValue NaN;
    String ancillary_variables "analysed_sst_uncertainty mask";
    String cell_methods "time: percentile (value=90) within days time: percentile (value=90) over years";
    String depth "20 cm";
    String description "Daily 90th percentile of analysed_sst over the reference period 1987-2016, used as the marine-heatwave threshold base following Hobday et al. (2016). Two-pass smoothing applied: 11-day rolling mean (equivalent to Hobday et al. (2016) +/-5-day cross-DOY pooling) followed by a 31-day rolling mean.";
    String ioos_category "Temperature";
    String long_name "Climatological daily 90th percentile of analysed_sst";
    Float64 missing_value NaN;
    Int32 percentile 90;
    String reference_period "1987-2016";
    String source "ATSR<1,2>-ESACCI-L3U-v3.0, AATSR-ESACCI-L3U-v3.0, SLSTR<A,B>-ESACCI-L3U-ICDR-v3.0 AVHRR<06,07,08,09,10,11,12,14,15,16,17,18,19>_G-ESACCI-L3U-v3.0, AVHRRMT<A,B>-ESACCI-L3U-v3.0, AMSR<E,2>-ESACCI-L2P-v2.0";
    String standard_name "sea_water_temperature";
    String units "degree_K";
  }
  threshCS {
    UInt32 _ChunkSizes 1, 1, 7200;
    Float64 _FillValue NaN;
    Float64 colorBarMaximum 5.0;
    Float64 colorBarMinimum -5.0;
    String colorBarPalette "BlueWhiteRed";
    String description "Threshold anomaly (p10 minus mean) used to define marine cold spells following Hobday et al. (2016). A maximum value of -0.01 is enforced to avoid zero/positive thresholds.";
    String ioos_category "Temperature";
    String long_name "Marine cold-spell threshold anomaly (p10 minus climatological mean)";
    Float64 missing_value NaN;
    String reference_period "1987-2016";
    String units "degree_K";
  }
  threshHW {
    UInt32 _ChunkSizes 1, 1, 7200;
    Float64 _FillValue NaN;
    Float64 colorBarMaximum 5.0;
    Float64 colorBarMinimum -5.0;
    String colorBarPalette "BlueWhiteRed";
    String description "Threshold anomaly (p90 minus mean) used to define marine heatwaves following Hobday et al. (2016). A minimum value of +0.01 is enforced to avoid zero/negative thresholds.";
    String ioos_category "Temperature";
    String long_name "Marine heatwave threshold anomaly (p90 minus climatological mean)";
    Float64 missing_value NaN;
    String reference_period "1987-2016";
    String units "degree_K";
  }
  NC_GLOBAL {
    String _NCProperties "version=2,netcdf=4.8.1,hdf5=1.12.2";
    String CDI "Climate Data Interface version 2.1.1 (https://mpimet.mpg.de/cdi)";
    String cdm_data_type "Grid";
    String CDO "Climate Data Operators version 2.1.1 (https://mpimet.mpg.de/cdo)";
    String Climatology "1987-2016";
    String Conventions "CF-1.8, COARDS, ACDD-1.3";
    String creator_email "algh@dmi.dk";
    String creator_name "Alex Hayward";
    Float64 Easternmost_Easting 179.97500610351562;
    String Experiment "Moving Average 2016";
    Float64 geospatial_lat_max 89.9749984741211;
    Float64 geospatial_lat_min -89.9749984741211;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 179.97500610351562;
    Float64 geospatial_lon_min -179.97500610351562;
    String geospatial_lon_units "degrees_east";
    String history 
"2026-07-03T11:14:32Z (local files)
2026-07-03T11:14:32Z http://erddap.dmi.dk/erddap/griddap/mhw_raw_1987_2016_climatology_90p_clim.das";
    String infoUrl "https://doi.org/10.3030/101136548";
    String institution "Danish Meteorological Institute";
    String license 
"The data may be used and redistributed for free but is not intended
for legal use, since it may contain inaccuracies. Neither the data
Contributor, ERD, NOAA, nor the United States Government, nor any
of their employees or contractors, makes any warranty, express or
implied, including warranties of merchantability and fitness for a
particular purpose, or assumes any legal liability for the accuracy,
completeness, or usefulness, of this information.";
    Float64 Northernmost_Northing 89.9749984741211;
    Int32 Percentile_Thresholds 10, 90;
    String project_code "ObsSea4Clim";
    String project_DOI "https://doi.org/10.3030/101136548";
    String project_edmerp "13765";
    String project_edmerp_uri "https://edmerp.seadatanet.org/report/13765";
    String project_id "101136548";
    String project_name "Ocean observations and indicators for climate and assessments";
    String project_statement "Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for them.";
    String projectName "1987_90_5";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing -89.9749984741211;
    String summary "Marine-heatwave product (Hobday et al. framework) from ESA SST-CCI. Experiment: Moving Average 2016. Climatology baseline: 1987-2016. Path: Raw/1987_2016_Climatology/90p/climatology. ObsSea4Clim project (EU grant 101136548).";
    String title "MHW Raw / 1987_2016_Climatology / 90p / climatology";
    Float64 Westernmost_Easting -179.97500610351562;
    Int32 xRange -180, 180;
    Int32 yRange -90, 90;
  }
}

Using griddap to Request Data and Graphs from Gridded Datasets

griddap lets you request a data subset, graph, or map from a gridded dataset (for example, sea surface temperature data from a satellite), via a specially formed URL. griddap uses the OPeNDAP (external link)

Data Access Protocol (DAP) (external link)

and its projection constraints (external link)

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

(easy) You can get data by using the dataset's Data Access Form. It makes the URL for you.
(easy) You can make a graph or map by using the dataset's Make A Graph form. It makes the URL for you.
(not hard) You can bypass the forms and get the data or make a graph or map by generating the URL by hand or with a computer program or script.

griddap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/griddap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/griddap/jplMURSST41.htmlTable?analysed_sst[(2002-06-01T09:00:00Z)][(-89.99):1000:(89.99)][(-179.99):1000:(180.0)]
Thus, the query is often a data variable name (e.g., analysed_sst), followed by [(start):stride:(stop)] (or a shorter variation of that) for each of the variable's dimensions (for example, [time][latitude][longitude]).

For details, see the griddap Documentation.