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Climate Change - November 2009
Interview Date: December 2009
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Robert Hijmans Robert Hijmans
From the Special Topic of Climate Change

In the Research Front Map " Climate Change and Species Distributions," which is part of our Special Topics analysis of Climate Change research over the past decade, the paper "Very high resolution interpolated climate surfaces for global land areas," (Hijmans RJ, et al., Int. J. Climatol. 25[15]: 1965-78, December 2005), has 262 cites. According to Essential Science IndicatorsSM from Thomson Reuters, citations to this paper now total 301 up to August 31st of this year.

Lead author Dr. Robert J. Hijmans's record in the database includes 34 papers cited a total of 1,189 times between January 1, 1999 and August 31, 2009. Dr. Hijmans is an Assistant Professor in the Department of Environmental Science and Policy at the University of California, Davis.

Below, he talks with about this paper and the importance of observed weather data in climate change research.

 Would you please describe the significance of your paper and why it is highly cited?

There is a strong interest in understanding and modeling the geographic distribution of organisms as a function of climate and other factors. Species distribution modeling (also known as environmental or ecological niche modeling) is a particularly frequently used method in biogeography, ecology, evolution, and conservation biology. In a species distribution model, the climate at known sites of occurrence of an organism is used to infer its climatic requirements and to predict its geographic range. But there are also applications of other types of climate-driven models—for example, in epidemiology and agriculture.

These models are used to understand geographic distributions under current conditions, but are also used to predict distributions across space and time. For example, to investigate the likely response of a species to projected future climates or to reconstructed paleo-climates, or to evaluate how well a tree species from one continent might grow somewhere in another continent, whether as an invasive or as an economically useful species.

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This type of modeling is done with climate data on a grid (raster) that can be obtained through interpolation of observations made at weather stations. In our paper we describe the "WorldClim" database that consists of climate grids with a spatial resolution of 1 km2 for the 1950-2000 period. The database includes monthly precipitation and maximum and minimum temperature. Previously available global climate data had a spatial resolution of about 324 km2. This is rather coarse for areas with sharp climate gradients, such as found in mountain environments.

The database is also used for statistically downscaling and calibrating projected future climate data. Climate models do not correctly predict current climate patterns for all areas. To circumvent this problem, one can interpolate the modeled change in climate and apply these to the WorldClim data to provide an estimate of future climate relative to observed, rather than to modeled, historical conditions.

 How did you become involved in this research, and were there any particular successes or obstacles that stand out?

We started the development of WorldClim because we needed high-resolution climate data for our research on species distributions. We did not plan to do this work as we were merely interested in using these data, and not in developing the database. We started with Ecuador, then expanded our work to cover the Americas, and having come that far we decided to do the whole (terrestrial) world, so that this "spin-off" would become more generally useful.

A major obstacle in this work is to get access to primary weather station data. For most countries it remains very difficult, or very expensive, to get access to weather data, even though these data were collected with public funding. Fortunately, we were able to build on earlier compilations by the Global Historical Climatology Network and the International Center for Tropical Agriculture, among others. Even though the total number of weather stations we used is quite high (about 46,000 for precipitation), there are many more stations and our coverage is rather sparse in some areas.

 Where do you see your research and the broader field leading in the future?

Much can be done to improve the interpolated climate data. Using more (and high quality) climate station data is probably most important. In addition, interpolation techniques can be improved, e.g. by using different algorithms and additional co-variables. There is a growing archive of satellite observations of weather data that could be used. Future versions of our database will have more climate variables and also have estimates of uncertainty.

 What are the implications of your work for this field?

Despite the broad interest in climate change, and grave concerns about it, there appears to be an absence of political leadership to make available the most basic data needed for such research: observed weather data. Our accidental role in creating WorldClim suggests there is also insufficient interest in, or support for, the compilation of detailed global geographic databases that are needed to understand global change. This includes climate data, but also land cover and land use and social and economic data. WorldClim is freely available on the web and its frequent use illustrates the importance of breaking barriers to data access, and of compiling detailed global geographic databases.

Robert J. Hijmans, Ph.D.
Department of Environmental Science and Policy
University of California, Davis
Davis, CA, USA

Robert Hijmans's current most-cited paper in Essential Science Indicators, with 340 cites:
Elith J, et al., "Novel methods improve prediction of species' distributions from occurrence data," Ecography 29(2): 129-151, April 2006. Source: Essential Science Indicators from Thomson Reuters.


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Special Topics : Climate Change : Robert Hijmans Interview - Special Topic of Climate Change