Kaicun Wang: Optimizing Data on Climate Change

Kaicun Wang

If climate scientist Kaicun Wang has any deep personal reflections on climate change and its implications for humanity, he doesn’t seem inclined to share them. Instead, he concentrates on the data at hand. “I don’t do anything involving forecasting or mitigation,” says Wang, based at Beijing Normal University, China. “What I try to do is understand past patterns in climate change, quantify the uncertainty of observed data, and work to improve climate-change detection.”

After beginning his academic career with studies on the complicated interaction between land and atmosphere, Wang actually turned his attention to the specifics of data collection: meteorological observations and remote sensing from satellites. The data, inevitably, demanded analysis. “Step by step,” says Wang, “I worked my way back to the field of atmospheric science, investigating how land-atmosphere interaction impacts climate change.”


In particular, Wang has concentrated on a central phenomenon in the interface between land and atmosphere: evapotranspiration. In this process, prompted by solar radiation, water changes from liquid to gas and enters the atmosphere via evaporation from land and water surfaces and via transpiration from plants. The result is clouds and precipitation. Moreover, having absorbed the sun’s radiation, land actually heats the atmosphere. These various dynamics, of course, are a central energy source and driver of climate change.

Wang’s most-cited paper in the Web of Science Core Collection, coauthored with Robert Dickinson, his mentor during his postdoctoral studies at the University of Texas at Austin, reviews terrestrial evapotranspiration and attendant matters of modeling and climatic variation (K.C. Wang, R.E. Dickinson, Reviews of Geophysics, 50, DOI: 10.1029/2011RG000373, May 2012; now cited more than 225 times.) Another paper on which Wang was a leading author, from 2010, discussed satellite evidence for an increase in global land surface evapotranspiration from 1982 to 2002 (K.C. Wang, et al., Journal of Geophysical Research-Atmospheres, D20113, DOI: 10.1029/2010JD01384, 2010).

An abiding challenge for Wang and his colleagues has been – far from a lack of data – a lack of complete, uniform data. For example, figures collected from hundreds of weather stations may be inconsistent due to the sensing equipment having been changed over time. Information on such key factors as solar radiation may be sparse.

Some of Wang’s recent work has concentrated on input data and devising improved methods for calculating evapotranspiration and its effects. By combining different data sources, he’s worked to arrive at a better set of data on solar radiation. Based on this work, he found that observational bias – including variations in instrument sensitivity and the periodic replacement of instruments – explains the discrepancies between the observed and simulated variability of surface incident solar radiation in the decades spanning the 1950s and 1980s. He has also concentrated on other variables, such as wind speed and relative humidity. “Lately,” he notes, “I’ve focused on how to gauge the input data more accurately.”

Aerosols and surface solar radiation

Wang’s other key contributions center on atmospheric aerosols. A paper he counts among his most significant appeared in Science in 2009. This report examines the phenomenon of “global dimming” – a lessening surface solar radiation – due to increased levels of aerosols and the subsequent decrease in solar radiation reaching Earth’s surface, between 1973 and 2007. This dimming effect is less pronounced over Europe, where air-quality standards are more stringent, but is more prevalent over south and east Asia, South America, Australia, and Africa, resulting in, as the paper notes, “a net global dimming over land” (K.C. Wang, et al., Science, 323 [5920]: 1468-70, 2009; cited in Web of Science more than 155 times).

Regional Warming

The geographic differences in clear-sky visibility point to another aspect of Wang’s work, and to a comparatively recent shift in climate studies: a change in emphasis from global to regional warming. “Currently,” says Wang, “scientists are more interested in regional climate change, and regional climate change is actually more complex. A major issue is how land-atmosphere interaction affects the process.”

Again, the challenge for Wang and his colleagues is deciding upon the best data to employ. Even something as apparently simple as determining air temperature can present inconsistency and uncertainty. “The dataset we currently use for air temperature is not a real measurement of mean temperature,” he says. “It’s only an average from daily maximum and minimum temperatures. It’s better if we use the maximum temperature rather than the mean. And, in the study of the land-atmosphere interaction, another parameter not currently being used is ‘skin surface’ temperature data available from satellites and from Chinese weather stations. This is better than the standard collection of ‘air temperature,’ which some people call ‘surface temperature’ but is actually the ‘near-surface’ temperature, sometimes called the ‘two-meter’ temperature.” (K.C. Wang, Scientific Reports, 4, 4637: DOI: 10.1038/scrp04637, 2014; J. Du, et al., Atmospheric Chemistry and Physics, 17: 4931-44, 2017).

Regional climate change is central to Wang’s latest work, studying varying rates of temperature change in China itself – a warming trend in the north, with cooling in the south. Taking into account solar radiation and other variables, Wang is working on how computer models of the system can reproduce this kind of change.

Asked whether he’s optimistic about the challenges of a changing climate, Wang makes plain that his focus is on the data and maximizing its usefulness. “We have ground-based observation from weather stations, we have satellite data, and we have modelling studies,” he says. “We have a lot of data that are not being used in current studies, such as sunshine-duration visibility and land-surface temperature measurements. These data all have their own advantages, and we can use that. My first goal is to know how the climate is changing. The second is to know why.”