Downscaling drivers of global environmental change: Enabling use of global SRES scenarios at the national and grid levels

Abstract

Global environmental change scenarios typically distinguish between about 10–20 global regions. However, various studies need scenario information at a higher level of spatial detail. This paper presents a set of algorithms that aim to fill this gap by providing downscaled scenario data for population, gross domestic product (GDP) and emissions at the national and grid levels. The proposed methodology is based on external-input-based downscaling for population, convergence-based downscaling for GDP and emissions, and linear algorithms to go to grid levels. The algorithms are applied to the IPCC-SRES scenarios, where the results seem to provide a credible basis for global environmental change assessments.