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

Fick, Stephen E.; Hijmans, Robert J.

doi:10.1002/joc.5086

Cited by 16,088

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

Fick, Stephen E., Hijmans, Robert J.·2017·International Journal of Climatology

DOI 10.1002/joc.5086

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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.

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Bioclimatic variables, such as seasonality and extreme or limiting environmental factors, are key to determine the distribution of many plants and animals.
“Bioclimatic variables, such as seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environmental factors (e.g., temperature of the coldest and warmest month) are key to determine the distribution of many plants and animals (Fick & Hijmans 2017);”
From A maxent predictive model for hunter-gatherer sites in the southern pampas, argentina (2021) · cites this paper as Fick & Hijmans 2017

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Authors: Fick, S. E., & Hijmans, R. J.
Affiliations: Department of Plant Sciences University of California Davis CA USA
Department of Environmental Science and Policy University of California Davis CA USA
ORCID: Fick, Stephen E.
Venue: International Journal of Climatology
Publisher: Wiley
Published: 2017-05-15
Volume: 37
Issue: 12
Pages: 4302-4315
ISSN: 0899-8418
1097-0088
DOI: 10.1002/joc.5086
Cited by: 16,088
References: 0
Language: en
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Funders: Feed the Future Sustainable Intensification Innovation Lab (SIIL) (AID-OOA-L-14-00006)

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

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