The potential to reduce uncertainty in regional runoff projections from climate models

Flavio Lehner (), Andrew W. Wood, Julie A. Vano, David M. Lawrence, Martyn P. Clark and Justin S. Mankin
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Flavio Lehner: National Center for Atmospheric Research
Andrew W. Wood: National Center for Atmospheric Research
Julie A. Vano: National Center for Atmospheric Research
David M. Lawrence: National Center for Atmospheric Research
Martyn P. Clark: University of Saskatchewan
Justin S. Mankin: Dartmouth College

Nature Climate Change, 2019, vol. 9, issue 12, 926-933

Abstract: Abstract Increasingly, climate change impact assessments rely directly on climate models. Assessments of future water security depend in part on how the land model components in climate models partition precipitation into evapotranspiration and runoff, and on the sensitivity of this partitioning to climate. Runoff sensitivities are not well constrained, with CMIP5 models displaying a large spread for the present day, which projects onto change under warming, creating uncertainty. Here we show that constraining CMIP5 model runoff sensitivities with observed estimates could reduce uncertainty in runoff projection over the western United States by up to 50%. We urge caution in the direct use of climate model runoff for applications and encourage model development to use regional-scale hydrological sensitivity metrics to improve projections for water security assessments.

Date: 2019
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DOI: 10.1038/s41558-019-0639-x

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