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WorldClim_Historical_bio

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Field Value
Description Historical climate data for global land areas.
Folder /datasets/Covariates/WorldClim_Historical_bio
Discipline Covariates / Climate Science / Geography
DOI 10.1002/joc.5086
Link Access Data
Public true
Publication Date 2017-05-17
Downloaded 2024-11-25
Time Resolution 1970-2000
Spatial Resolution 30 arc-seconds (~1 km)
Data Type GIS Geotiff file
Dataset Size 11G
Number of Files 19
Usage 
$ module avail
$ module load datasets
$ module load Covariates/WorldClim_Historical_bio/2017-05-17
Usage Policy Link
Usage Policy This dataset is freely available for academic and other non-commercial use. Redistribution or commercial use is not permitted without prior written permission from the data providers. Users may use WorldClim data to create maps and figures for inclusion in academic publications, including those published under open-access licenses such as CC BY. When using or referencing the data, proper acknowledgment of the WorldClim project and citation of the relevant dataset version and publication are required. For inquiries regarding permissions or licensing for redistribution or commercial use, users should contact the WorldClim team at info@worldclim.org.
Citation Fick, S.E. and Hijmans, R.J. (2017), WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol, 37: 4302-4315. https://doi.org/10.1002/joc.5086
BibTeX
📜 View BibTeX citation
@article{https://doi.org/10.1002/joc.5086,
author = {Fick, Stephen E. and Hijmans, Robert J.},
title = {WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas},
journal = {International Journal of Climatology},
volume = {37},
number = {12},
pages = {4302-4315},
keywords = {interpolation, climate surfaces, WorldClim, MODIS, land surface temperature, cloud cover, solar radiation, wind speed, vapour pressure},
doi = {https://doi.org/10.1002/joc.5086},
url = {https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.5086},
eprint = {https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.5086},
abstract = {ABSTRACT We created a new dataset of spatially interpolated monthly climate data for global land areas at a very high spatial resolution (approximately 1 km2). We included monthly temperature (minimum, maximum and average), precipitation, solar radiation, vapour pressure and wind speed, aggregated across a target temporal range of 1970–2000, using data from between 9000 and 60 000 weather stations. Weather station data were interpolated using thin-plate splines with covariates including elevation, distance to the coast and three satellite-derived covariates: maximum and minimum land surface temperature as well as cloud cover, obtained with the MODIS satellite platform. Interpolation was done for 23 regions of varying size depending on station density. Satellite data improved prediction accuracy for temperature variables 5–15\% (0.07–0.17 °C), particularly for areas with a low station density, although prediction error remained high in such regions for all climate variables. Contributions of satellite covariates were mostly negligible for the other variables, although their importance varied by region. In contrast to the common approach to use a single model formulation for the entire world, we constructed the final product by selecting the best performing model for each region and variable. Global cross-validation correlations were ≥ 0.99 for temperature and humidity, 0.86 for precipitation and 0.76 for wind speed. The fact that most of our climate surface estimates were only marginally improved by use of satellite covariates highlights the importance having a dense, high-quality network of climate station data.},
year = {2017}
}