Volume 37, Issue 12 p. 4302-4315
Research Article

WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas

Stephen E. Fick

Corresponding Author

Stephen E. Fick

Department of Plant Sciences, University of California, Davis, CA, USA

Correspondence to: S. E. Fick, Stockholm Environment Institute, Linnégatan 87 D, 115 23 Stockholm, Sweden. E-mail: [email protected]Search for more papers by this author
Robert J. Hijmans

Robert J. Hijmans

Department of Environmental Science and Policy, University of California, Davis, CA, USA

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First published: 15 May 2017
Citations: 8,045

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.