"Statistical downscaling takes historically observed relationships between the large-scale atmospheric state and a local climate response, and applies them to global climate model projections," said Robert Crane, professor of geography, Penn State. "We applied the downscaling methodology to the climate model projections."
The team's goal was to predict malaria transmission potential within the four locations. They used a simple mathematical model that describes the influence of temperature on the ability of adult mosquitoes to transmit malaria parasites to compare the predictions they obtained in the four locations with the predictions from the coarse-scale model simulations.
"Fine-scale predictions of malaria risk will be better tailored to the needs of local communities and can improve local adaptation and mitigation strategies," Paaijmans said.
The results appear in the June 19 issue of Climatic Change.
The team found that the conventional approach of using coarse-scale climate models yielded different predictions for future changes in malaria transmission potential in the four locations than when they applied the downscaling methodology.
"Using the raw coarse-scale model simulation results sometimes overestimated and sometimes underestimated the effects of climate change for particular locations compared with our downscaled model results," Thomas said.
Specifically, the team's downscaled model results predicted large increases in future malaria transmission potential in the cool upland sites, but reduced transmission in the hot savannah-like site. The results also predicted an increase in transmission potential in the warm lower-altitude site, but the increase was less pronounced when using the d
|Contact: A'ndrea Elyse Messer|