It's tempting to view global warming on, well, a global scale. However, when we think about how climate change affects human and biological systems, it's often the local impacts that matter most. We want to know how things are going to change where we live, not on some abstract global scale.

In the past, local impacts have been very difficult for scientists to assess. One of our most useful tools, climate computer models, are best used to predict how the entire globe will change. These computer models work by subdividing the world into millions of elements or grid boxes. Equations describing conservation of mass, energy, and other processes are solved at each grid box. Then, the grid boxes are assembled to recreate the geometry of the entire Earth system. Just like a puzzle image takes shape when the pieces are brought together, so to the climate takes shapes as the grid boxes are brought together.

But, the weak link in this process is that with today’s computer power, the grid boxes are too large to give a true picture of local variations. We say the grid is “coarse” at the regional scale. Scientists have figured out a clever way around this problem; a way to use coarse global climate programs to get regional information.



The method, which is often called downscaling, was a central tool in a paper just published by a team of scientists including Dr. Matthew Thomas and Dr. Michael Mann, both from Penn State University. This study tackled the problem of malaria – a devastating disease that causes enormous economic and human costs around the world. It is a disease I’ve seen firsthand in my travel and work in east Africa.

In the study, published in the journal Climatic Change, the authors acknowledge that malaria and the mosquitoes that carry the parasite are sensitive to daily variations and very local conditions. They downscale global climate models “to provide high-resolution temperature data for four different sites (in Kenya) and show that although outputs from the global and downscaled models predict diverse but qualitatively similar effects,” and some of the modeling approaches led to quite different findings. For instance, the global models underestimated the impact of climate change in hot and cold climate zones. On the other hand, global models over predict the effect in moderate regions.

When asked about his study, Dr. Mann told me,

This is one of the first studies to attempt to explore how climate change might impact conditions at the local level. “The results suggest the possibility that population centers in cool highland regions could be more vulnerable than previously thought, while other equally large lowland areas might be less vulnerable. But this would have to be confirmed with more detailed modeling assessments that take into account the full suite of environmental and socio-economic factors that ultimately determine risk of malaria.

While we think this paper is of purely academic interest, or that its implications are limited to malaria, upon further review, the findings are more significant. First, studies like this can help local officials better plan for a warmer and more extreme climate. Secondly, this study charts a pathway for other studies where the outcome depends more on microclimate than macroclimate. We should all be excited about research like this that will help us plan for a future in a way that mitigates some of the costs that global societies will bear worldwide.