Climate Dynamics

, Volume 40, Issue 3–4, pp 709–729 | Cite as

Using climate impacts indicators to evaluate climate model ensembles: temperature suitability of premium winegrape cultivation in the United States

  • Noah S. DiffenbaughEmail author
  • Martin Scherer


We explore the potential to improve understanding of the climate system by directly targeting climate model analyses at specific indicators of climate change impact. Using the temperature suitability of premium winegrape cultivation as a climate impacts indicator, we quantify the inter- and intra-ensemble spread in three climate model ensembles: a physically uniform multi-member ensemble consisting of the RegCM3 high-resolution climate model nested within the NCAR CCSM3 global climate model; the multi-model NARCCAP ensemble consisting of single realizations of multiple high-resolution climate models nested within multiple global climate models; and the multi-model CMIP3 ensemble consisting of realizations of multiple global climate models. We find that the temperature suitability for premium winegrape cultivation is substantially reduced throughout the high-value growing areas of California and the Columbia Valley region (eastern Oregon and Washington) in all three ensembles in response to changes in temperature projected for the mid-twenty first century period. The reductions in temperature suitability are driven primarily by projected increases in mean growing season temperature and occurrence of growing season severe hot days. The intra-ensemble spread in the simulated climate change impact is smaller in the single-model ensemble than in the multi-model ensembles, suggesting that the uncertainty arising from internal climate system variability is smaller than the uncertainty arising from climate model formulation. In addition, the intra-ensemble spread is similar in the NARCCAP nested climate model ensemble and the CMIP3 global climate model ensemble, suggesting that the uncertainty arising from the model formulation of fine-scale climate processes is not smaller than the uncertainty arising from the formulation of large-scale climate processes. Correction of climate model biases substantially reduces both the inter- and intra-ensemble spread in projected climate change impact, particularly for the multi-model ensembles, suggesting that—at least for some systems—the projected impacts of climate change could be more robust than the projected climate change. Extension of this impacts-based analysis to a larger suite of impacts indicators will deepen our understanding of future climate change uncertainty by focusing on the climate phenomena that most directly influence natural and human systems.


Climate change Climate impacts CMIP3 NARCCAP RegCM3 Winegrape 



We thank two anonymous reviewers for insightful and constructive comments. We wish to thank the North American Regional Climate Change Assessment Program (NARCCAP) for providing the NARCCAP data used in this paper. NARCCAP is funded by the National Science Foundation (NSF), the U.S. Department of Energy (DoE), the National Oceanic and Atmospheric Administration (NOAA), and the U.S. Environmental Protection Agency Office of Research and Development (EPA). We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of the CMIP3 dataset is provided by the Office of Science, U.S. Department of Energy. We thank the National Centers for Environmental Prediction (NCEP) for providing access to the North American Regional Reanalysis (NARR) dataset, and the PRISM Climate Group and Oregon State University for providing access to the PRISM observational temperature dataset. Our RegCM3 climate model experiments were generated and stored using computing resources provided by the Rosen Center for Advanced Computing (RCAC) at Purdue University, and our analyses of all datasets were performed using computing resources provided by the Center for Computational Earth and Environmental Science (CEES) at Stanford University. The research reported here was supported by NSF award 0955283 and NIH award 1R01AI090159-01.


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© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Department of Environmental Earth System Science, Woods Institute for the EnvironmentStanford UniversityStanfordUSA
  2. 2.Department of Earth and Atmospheric Sciences, Purdue Climate Change Research CenterPurdue UniversityWest LafayetteUSA

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