Downscaling reveals diverse effects of anthropogenic climate warming on the potential for local environments to support malaria transmission

Krijn P Paaijmans,Matthew B Thomas,Liang Ning,Robert G Crane,Michael E Mann,Kathleen V Schreiber,Justine I Blanford

doi:10.1007/s10584-014-1172-6

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.

Highlights

Several studies predict rising global temperatures to increase the burden of malaria (Martens et al 1999; Pascual et al 2006)
The days from the present (1981– 2000) and future (2046–2065) General Circulation Model (GCM) simulations are mapped to the Self-Organizing Map (SOM) and a paired maximum and minimum temperature randomly selected from the appropriate cumulative frequency distribution (CFD)
The ensemble means from the raw GCM output predict marginally lower increases in mean annual temperature, but slightly larger decreases in daily temperature range (DTR) (0.6, 0.6, 0.5 and 0.4 for Kericho, Kitale, Kisumu and Garissa, respectively)

Summary

Introduction

Several studies predict rising global temperatures to increase the burden of malaria (Martens et al 1999; Pascual et al 2006). Climate model (coupled Atmosphere–ocean General Circulation Model) simulation data are available and can be used for projecting potential climate change impacts on biophysical processes (Solomon et al 2007; World Climate Research Programme) These General Circulation Model (GCM) data most confidently characterize climate changes at relatively coarse spatial and temporal scales (i.e. averaged over large regions and time through sub-continental monthly and seasonal averages). They are considerably less valid in characterizing local (point) projections on daily timescales. Different climate models, even when constructed with the same basic large-scale physics and driven by the same climate change forcing, often give conflicting results owing, for example, to the way sub grid-scale processes such as atmospheric turbulence and cloud cover are represented (Meehl et al 2007)

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Results

Conclusion