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Downscaling reveals diverse effects of anthropogenic climate warming on the potential for local environments to support malaria transmission |
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| Authors: | Krijn P Paaijmans Justine I Blanford Robert G Crane Michael E Mann Liang Ning Kathleen V Schreiber Matthew B Thomas |
| Institution: | 1. Center for Infectious Disease Dynamics and Department of Entomology, The Pennsylvania State University, University Park, PA, 16802, USA 2. Barcelona Centre for International Health Research (CRESIB, Hospital Clínic-Universitat de Barcelona), Barcelona, Spain 3. GeoVISTA Center, Department of Geography, The Pennsylvania State University, University Park, PA, 16802, USA 4. AESEDA, Department of Geography, The Pennsylvania State University, University Park, PA, 16802, USA 5. Department of Meteorology and Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, PA, 16802, USA 6. Northeast Climate Science Center, Department of Geosciences, University of Massachusetts - Amherst, Amherst, MA, 01003, USA 7. Department of Geography, Millersville University of Pennsylvania, Millersville, PA, 17551, USA |
| Abstract: | The potential impact of climate warming on patterns of malaria transmission has been the subject of keen scientific and policy debate. Standard climate models (GCMs) characterize climate change at relatively coarse spatial and temporal scales. However, malaria parasites and the mosquito vectors respond to diurnal variations in conditions at very local scales. Here we bridge this gap by downscaling a series of GCMs to provide high-resolution temperature data for four different sites and show that although outputs from both the GCM and the downscaled models predict diverse but qualitatively similar effects of warming on the potential for adult mosquitoes to transmit malaria, the predicted magnitude of change differs markedly between the different model approaches. Raw GCM model outputs underestimate the effects of climate warming at both hot (3-fold) and cold (8–12 fold) extremes, and overestimate (3-fold) the change under intermediate conditions. Thus, downscaling could add important insights to the standard application of coarse-scale GCMs for biophysical processes driven strongly by local microclimatic conditions. |
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