HighResCoralStress: Observed and statistically downscaled CMIP6 projections of daily sea surface temperature (SST) at 0.01°/1 km spatial resolution for the global coral reef area from 1985 to 2100.
The HighResCoralStress dataset comprises high spatial resolution (0.01°/1 km) daily sea surface temperature (SST) for the global coral reef area for past (1985-2019) and future (2020-2100) time periods. There are 12 coral reef regions based on those described by McWilliam et al. (2018). They vary in their functional redundancy and so indicate susceptibility to ecological changes with climate change. Statistically downscaled Coupled Model Intercomparison Project 6 (CMIP6) projections of daily SST are provided for 420,334 1 km coral reef pixels in 12 coral reef regions for four shared socio-economic pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) for the time period (1985-2100).
There are separate downscaled CMIP6 files for each region and SSP. The 1 km observed dataset used in the statistical downscaling is also provided for the period (1985-2019). CMIP6 projections of SST were statistically downscaled from their native model resolution (25-100 km) to 1 km spatial resolution using asynchronous linear regression. Full details of the statistical downscaling methodology and source datasets used in this project can be found in Dixon et al. (2022).
Coral reefs globally are threatened by rising ocean temperatures and increasing frequency and severity of thermal stress events. High resolution sea surface temperature (SST) projections are a useful tool in climate vulnerability assessments, ecological modelling, spatial planning and conservation decision making for coral reefs around the world.
All source datasets used in the generation of HighResCoralStress are openly available and details can be found in Dixon et al. (2022). The authors request that you refer to this article before using the datasets, please refer to online resources on this record for links. Thermal stress metrics calculated using HighResCoralStress can be found at the following link: https://highrescoralstress.org/. The coordinates for the regional boundaries can be found in the NetCDF file attributes.
This work was undertaken by researchers from the University of Leeds, Texas Tech University and James Cook University funded by the Natural Environment Research Council (NERC) project DTP Spheres (NE/L002574/1).
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Public data: access to these data is available to both registered and non-registered users.
Use of these data is covered by the following licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/. When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Dataset was generated for 420,334 1 km coral reef pixels. The four 3-month seasonal periods used in the statistical downscaling were updated to start in December instead of January. For V1.0, dataset first created for 232,828 1 km coral reef pixels in 12 coral reef regions. Details of how the dataset was generated can be found in the following article: https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0000004. The outputs are archived on the British Oceanographic Data Centre (BODC)'s space at the Centre for Environmental Data Analysis (CEDA) and assigned a DOI. No quality control procedures were applied by BODC.
Model output has only been checked for CF convention compliance, otherwise supplied as-is.
Data are CF-Compliant NetCDF formatted data files
Statistical downscaling of daily SST for the global coral reef area
HighResCoralStress was generated by statistical downscaling of sea surface temperature (SST) projected by CMIP6 models for the global coral reef area. The global coral reef area was determined by extracting the latitude and longitude of 1 km global coral reef pixels from the UNEP World Conservation Monitoring Centre dataset (UNEP-WCMC, WorldFish Centre, WRI, TNC, 2010). The 1 km global coral reef pixels were split into 12 coral reef regions (McWilliam et al., 2018). Observed SST used in the statistical downscaling was a combination of the JPL MUR SST Analysis (Chin et al., 2017; 1 km) and ESA CCI SST Analysis (Merchant et al., 2016; 5 km) resulting in a single 1 km resolution dataset for each global coral reef pixel (01/01/1985 - 31/12/2019) - see S1 Appendix in Dixon et al. (2022) for more information. Data for reef pixels where CCI uses a climatology, likely due to missing data, and MUR does not were replaced with NOAA CRW CoralTemp SST (NOAA Coral Reef Watch, 2018; 5 km) - again see S1 Appendix in Dixon et al. (2022) for more information. CMIP6 model 'tos' output for 14 models and four Shared Socioeconomic Pathways (SSP) was interpolated longitudinally to fill missing data points, converted to 1 km resolution by bilinear interpolation and data extracted for each 1 km global coral reef pixel - see S2 Appendix in Dixon et al. (2022) for more information. For the statistical downscaling, linear regression models were generated for four seasonal periods for each global coral reef pixel using the ranked observed and simulated 1 km SST for a model training period (even years). Different combinations of polynomial trends (1st, 2nd and 3rd order) were removed from the observed and simulated SST datasets prior to downscaling and added back in after to maintain the long-term warming trend simulated by the model - see S3 Appendix in Dixon et al. (2022) for more information. The statistically downscaled SST for the historical period (1985-2019) was evaluated relative to observed SST for a model testing period (odd years) by calculating the root mean square error - see S3 Appendix in Dixon et al. (2022). The 'best' combination of polynomial trends was selected by finding the lowest root mean square error - see S3 Appendix in Dixon et al. (2022). Finally, the seasonal linear regression models were generated using all years in the historical time period (1985-2019) to statistically downscale the simulated SST (1985-2100). The method is described in more detail in Dixon et al. (2022).
- var_id: sst
- standard_name: sea_surface_temperature
- units: Celsius
- long_name: downscaled sea surface temperature
- units: degrees_north
- standard_name: latitude
- var_id: lat
- long_name: Latitude
- units: degrees_east
- standard_name: longitude
- var_id: lon
- long_name: Longitude
- long_name: time
- standard_name: time
- var_id: time
- units: days