I currently consider using WorldClim (v. 1.4) data for testing how plant communities respond to variations in temperature and precipitation in Greenland, but I am not entirely sure to what extent the data can be trusted. Has anyone tried validating WorldClim data against field measurements in the Arctic?
Another cynical answer... WC data are known to be full of errors, particularly at finer spatial resolutions. The bioclimatic variables 8, 9, 18, and 19 hold many spatial artifacts. I am perhaps not so concerned about the Arctic, but rather about the sparse representation of weather stations in the region... it means that the data will be all interpolation and very little information.
What are the alternatives to WorldClim datasets? Are any GIS compatible?
Dr Towe is asking about climate change calculations, I believe. The original question, however, was about the advisability of using WorldClim climate reconstructions. Mark Deka asks what are the alternatives ...
1. For the USA, the PRISM data are great, and can be merged to create different temporal windows.
2. For the world, the New et al. data set is probably the best-regarded: 1. New, M., M. Hulme, and P. Jones. 1997. A 1961-1990 mean monthly climatology of global land areas. Climatic Research Unit, University of East Anglia, Norwich, U.K.
3. Finally, probably the best alternative is to use climate-related dimensions of remote-sensing products. This avoids the need to interpolate, although it is not without its complications.
People may not be very happy with my suggestion of the New et al. data, as the spatial resolution is not fine. It is probably a more honest depiction of the detail with which we know about climate.
Other sources of climate data EcoClimate (http://ecoclimate.org/) and CliMond (https://www.climond.org/).
Is it possible and logical to use the findings of Escobar et al. 2014 in other parts of the world to exclude those bioclimatic variables from species distribution analysis that showed the spatial artifacts in their study? or, should everyone check this spatial anomaly for every study region of interest?
Escobar LE, Lira-Noriega A, Medina-Vogel G, Peterson AT. Potential for spread of the white-nose fungus (Pseudogymnoascus destructans) in the Americas: use of Maxent and NicheA to assure strict model transference. Geospatial health. 2014; 9(1):221–9.
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