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

Nature Climate Change volume 9, pages 926–933 (2019)Cite this article

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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.

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Fig. 1: Projected changes in temperature, precipitation, runoff and runoff efficiency.
Fig. 2: Absolute and relative bias in precipitation and runoff in Earth system models.
Fig. 3: Runoff sensitivities.
Fig. 4: Runoff projections and their relationship to runoff sensitivities.

Data availability

All data used in this study are publicly available. The CMIP5 simulations are available through PCMDI, the CESM simulations are available on earthsystemgrid.org, and the observational data are available through the respective institutions. Post-processed data can be obtained from the corresponding author.

Code availability

Code to produce the figures is available from the corresponding author.

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Acknowledgements

We thank A. Pendergrass, S. Swenson, E. Wahl, C. Milly, L. Gudmundsson, S. Seneviratne, M. Hoerling, J. Barsugli and N. Addor for discussions, and A. Swann for discussion and for providing the C4MIP simulations. This work benefited from discussions at a 2018 workshop on Colorado River climate sensitivity held at NOAA in Boulder, USA. We acknowledge the efforts of all those who contributed to producing the simulations and observational datasets. The National Center for Atmospheric Research is sponsored by the US National Science Foundation (NSF). F.L. is supported by NSF AGS-0856145, Amendment 87, by the Bureau of Reclamation under Cooperative Agreement R16AC00039, and the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the US Department of Energy’s Office of Biological & Environmental Research (BER) via NSF IA 1947282. A.W. is supported by the Bureau of Reclamation (CA R16AC00039), by the US Army Corps of Engineers (CSA 1254557). A.W. and J.A.W. are supported by the NASA Advanced Information Systems Technology program (award ID 80NSSC17K0541). D.M.L is partially supported by NSF INSPIRE grant (NSF-EAR-1528298) and by the RUBISCO Scientific Focus Area (SFA), which is sponsored by the Regional and Global Climate Modeling (RGCM) Program in the Climate and Environmental Sciences Division of the Office of Biological and Environmental Research in the US Department of Energy Office of Science. J.A.V. is supported by grant 80NSSC17K0541 from the NASA AIST program.

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Authors and Affiliations

  1. Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA

    Flavio Lehner & David M. Lawrence

  2. Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA

    Flavio Lehner, Andrew W. Wood & Julie A. Vano

  3. Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland

    Flavio Lehner

  4. Aspen Global Change Institute, Basalt, CO, USA

    Julie A. Vano

  5. Coldwater Laboratory, University of Saskatchewan, Canmore, Alberta, Canada

    Martyn P. Clark

  6. Department of Geography, Dartmouth College, Hanover, NH, USA

    Justin S. Mankin

  7. Department of Earth Sciences, Dartmouth College, Hanover, NH, USA

    Justin S. Mankin

  8. Division of Ocean and Climate Physics, Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA

    Justin S. Mankin

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Contributions

F.L. and A.W.W. conceived the study. F.L. conducted all analyses, constructed the figures and led the writing. All authors contributed to the interpretation of the results and the writing of the manuscript.

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Correspondence to Flavio Lehner.

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Lehner, F., Wood, A.W., Vano, J.A. et al. The potential to reduce uncertainty in regional runoff projections from climate models. Nat. Clim. Chang. 9, 926–933 (2019). https://doi.org/10.1038/s41558-019-0639-x

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