CLIPMACULL3
Description
CLIPMACULL3 dataset provides high-resolution climate variables for the Macaronesian archipelagos at a spatial resolution of 3×3 km per month, seasonal and annually. Temperature (max, mean and min), precipitation, radiation, cloud cover, humidity, and wind speed (mean and max) averages over different periods calculated through dynamic regionalisation models. It consists in NetCDF4 format with a grid cell resolution of 3×3 km, covering the region of 4 Macaronesian archipelagos calculated in Recent Past (1982-2019) and for the periods: 2030−2059, and 2070−2099, under the Shared Socioeconomic Pathway 1-2.6 (SSP1-2.6) and SSP5-8.5, for future. The non-hydrostatic WRF model, with boundary conditions from 14 global models (from CMI6), was used to perform the simulations. The dataset is provided in NetCDF4 format and complies with CF (Climate and Forecast) metadata conventions to ensure interoperability with climate analysis tools such as CDO, Panoply, and Python libraries (xarray, netCDF4). In "Recent Past and Future Projections" folder (or compressed file), files have been sort by archipelago and variable. This folder is refered to Recent Past data or Future Projections, just raw data averaged from WRF-model. Each name file corresponds to a single variable, archipelago, scenario, temporal aggregation level, and period. Temporal aggregations are provided at monthly, seasonal, and annual scales. To support users, the dataset includes metadata describing units, variable definitions, coordinate reference systems, and methodological details. The labeling convention follows the structure as an example: tas_azores-3_PGW_WRF_v391_month_Jan_1982-2019.nc In "Differences (Future - RecentPast)" directory (or compressed file), files have been sort by archipelago and variable. This folder is refered to the Difference between Recent Past and Future Projections, mean (Future-RecentPast) as raw data averaged from WRF-model. Each name file corresponds to a single variable, archipelago, scenario, temporal aggregation level, and period. Temporal aggregations are provided at monthly, seasonal, and annual scales. To support users, the dataset includes metadata describing units, variable definitions, coordinate reference systems, and methodological details. The provided code (representingg_and_downloading.py and index.html) implements a lightweight web service that facilitates the visualization and download of the selected maps and variables through a more intuitive and user-friendly interface. For proper operation, the code and both folders (Differences (Future - RecentPast) and Recent Past and Future Projections) must be located in the same directory. For the visualization code to function properly, both data folders and the script must be located in the same common directory. Furthermore, the host machine must have Python installed along with the netCDF4 and numpy libraries. To execute the application, open a terminal in that directory and run python launcher.py.
Files
Institutions
- Universidad de La LagunaCanary Islands, San Cristóbal de La Laguna
- Universidade Federal de São PauloSão Paulo, São Paulo