WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. (15th May 2017)

• Record Type: Journal Article
• Authors: Fick, Stephen E.; Hijmans, Robert J.
• Is Part Of: International journal of climatology. Volume 37:Number 12(2017)
• Page Start: 4302
• Page End: 4315
• Publication Date: 2017-05-15
• Subjects: interpolation -- climate surfaces -- WorldClim -- MODIS -- land surface temperature -- cloud cover -- solar radiation -- wind speed -- vapour pressure; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.5086
• Languages: English
• ISSNs: 0899-8418

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 km 2 ). 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. Abstract : We created a new global dataset of spatially interpolated monthly climate data at a 1 km 2 resolution, including monthly temperature, precipitation, solar radiation, vapor pressure and wind speed. Interpolations utilized MODIS satellite imagery, and instead of using a single model formulation for the entire world, we constructed the final product by selecting the best performing model for each region and variable. Satellite data improved prediction accuracy for temperature variables, particularly in areas with a low station density, but improvements were marginal for other variables, highlighting the importance of dense, high‐quality climate station data.