Climate Dynamics

, Volume 49, Issue 5–6, pp 1783–1799 | Cite as

Projections of 21st century climate of the Columbia River Basin

  • David E. Rupp
  • John T. Abatzoglou
  • Philip W. Mote


Simulations from 35 global climate models (GCMs) in the Coupled Model Intercomparison Project Phase 5 provide projections of 21st century climate in the Columbia River Basin under scenarios of anthropogenic activity given by Representative Concentration Pathways (RCP4.5 and RCP8.5). The multi-model ensemble 30-year mean annual temperature increases by 2.8 °C (5.0 °C) by late 21st century under RCP4.5 (RCP8.5) over the 1979–1990 baseline, with 18% (24%) more warming in summer. By late 21st century, annual precipitation increases by 5% (8%), with an 8% (14%) winter increase and a 4% (10%) summer decrease, but because some models project changes of opposite sign, confidence in these sign changes is lower than those for temperature. Four questions about temperature and precipitation changes were addressed: (1) How and why do climate projections vary seasonally? (2) Is interannual variability in seasonal temperature and precipitation projected to change? (3) What explains the large inter-model spread in the projections? (4) Do projected changes in climate depend on model skill? Changes in precipitation and temperature vary seasonally as a result of changes in large-scale circulation and regional surface energy budget, respectively. Interannual temperature variability decreases slightly during the cool seasons and increases in summer, while interannual precipitation variability increases in all seasons. The magnitude of regional warming is linked to models’ global climate sensitivity, whereas internal variability dominates the inter-model spread of precipitation changes. Lastly, GCMs that better reproduce historical climate tend to project greater warming and larger precipitation increases, though these results depend on the evaluation method.


CMIP5 Projections RCP4.5 RCP8.5 Columbia River Basin Variability Fidelity Variance partition 



This research was supported the U.S. Department of Energy-Bonneville Power Administration (cooperative agreement 00063182) and the U.S. Department of Agriculture, National Institute of Food and Agriculture (USDA-NIFA), via the Regional Approaches to Climate Change—Pacific Northwest Agriculture (REACCH PNA, USDA-NIFA grant 2011068002-30191) project. We thank Sihan Li for useful discussions, Darrin Sharp and Katherine Hegewisch for assistance with data processing, and two anonymous reviewers for their helpful comments. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

Supplementary material

382_2016_3418_MOESM1_ESM.pdf (351 kb)
Supplementary material 1 (PDF 350 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • David E. Rupp
    • 1
  • John T. Abatzoglou
    • 2
  • Philip W. Mote
    • 1
  1. 1.Oregon Climate Change Research Institute, College of Earth, Ocean, and Atmospheric SciencesOregon State UniversityCorvallisUSA
  2. 2.Department of GeographyUniversity of IdahoMoscowUSA

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