Climate Model Assessment of Changes in Winter–Spring Streamflow Timing over North America

Jonghun Kam Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, Alabama

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Thomas R. Knutson NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

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P. C. D. Milly U.S. Geological Survey, and NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

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Abstract

Over regions where snowmelt runoff substantially contributes to winter–spring streamflows, warming can accelerate snowmelt and reduce dry-season streamflows. However, conclusive detection of changes and attribution to anthropogenic forcing is hindered by the brevity of observational records, model uncertainty, and uncertainty concerning internal variability. In this study, the detection/attribution of changes in midlatitude North American winter–spring streamflow timing is examined using nine global climate models under multiple forcing scenarios. Robustness across models, start/end dates for trends, and assumptions about internal variability are evaluated. Marginal evidence for an emerging detectable anthropogenic influence (according to four or five of nine models) is found in the north-central United States, where winter–spring streamflows have been starting earlier. Weaker indications of detectable anthropogenic influence (three of nine models) are found in the mountainous western United States/southwestern Canada and in the extreme northeastern United States/Canadian Maritimes. In the former region, a recent shift toward later streamflows has rendered the full-record trend toward earlier streamflows only marginally significant, with possible implications for previously published climate change detection findings for streamflow timing in this region. In the latter region, no forced model shows as large a shift toward earlier streamflow timing as the detectable observed shift. In other (including warm, snow free) regions, observed trends are typically not detectable, although in the U.S. central plains we find detectable delays in streamflow, which are inconsistent with forced model experiments.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-17-0813.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. Jonghun Kam, jkam@eng.ua.edu

Abstract

Over regions where snowmelt runoff substantially contributes to winter–spring streamflows, warming can accelerate snowmelt and reduce dry-season streamflows. However, conclusive detection of changes and attribution to anthropogenic forcing is hindered by the brevity of observational records, model uncertainty, and uncertainty concerning internal variability. In this study, the detection/attribution of changes in midlatitude North American winter–spring streamflow timing is examined using nine global climate models under multiple forcing scenarios. Robustness across models, start/end dates for trends, and assumptions about internal variability are evaluated. Marginal evidence for an emerging detectable anthropogenic influence (according to four or five of nine models) is found in the north-central United States, where winter–spring streamflows have been starting earlier. Weaker indications of detectable anthropogenic influence (three of nine models) are found in the mountainous western United States/southwestern Canada and in the extreme northeastern United States/Canadian Maritimes. In the former region, a recent shift toward later streamflows has rendered the full-record trend toward earlier streamflows only marginally significant, with possible implications for previously published climate change detection findings for streamflow timing in this region. In the latter region, no forced model shows as large a shift toward earlier streamflow timing as the detectable observed shift. In other (including warm, snow free) regions, observed trends are typically not detectable, although in the U.S. central plains we find detectable delays in streamflow, which are inconsistent with forced model experiments.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-17-0813.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. Jonghun Kam, jkam@eng.ua.edu

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