Climate Dynamics

, Volume 48, Issue 7–8, pp 2191–2213 | Cite as

Seasonal spatial patterns of projected anthropogenic warming in complex terrain: a modeling study of the western US

  • David E. RuppEmail author
  • Sihan Li
  • Philip W. Mote
  • Karen M. Shell
  • Neil Massey
  • Sarah N. Sparrow
  • David C. H. Wallom
  • Myles R. Allen


Changes in near surface air temperature (ΔT) in response to anthropogenic greenhouse gas forcing are expected to show spatial heterogeneity because energy and moisture fluxes are modulated by features of the landscape that are also heterogeneous at these spatial scales. Detecting statistically meaningful heterogeneity requires a combination of high spatial resolution and a large number of simulations. To investigate spatial variability of projected ΔT, we generated regional, high-resolution (25-km horizontal), large ensemble (100 members per year), climate simulations of western United States (US) for the periods 1985–2014 and 2030–2059, the latter with atmospheric constituent concentrations from the Representative Concentration Pathway 4.5. Using the large ensemble, 95 % confidence interval sizes for grid-cell-scale temperature responses were on the order of 0.1 °C, compared to 1 °C from a single ensemble member only. In both winter and spring, the snow-albedo feedback statistically explains roughly half of the spatial variability in ΔT. Simulated decreases in albedo exceed 0.1 in places, with rates of change in T per 0.1 decrease in albedo ranging from 0.3 to 1.4 °C. In summer, ΔT pattern in the northwest US is correlated with the pattern of decreasing precipitation. In all seasons, changing lapse rates in the low-to-middle troposphere may account for up to 0.2 °C differences in warming across the western US. Near the coast, a major control of spatial variation is the differential warming between sea and land.


Regional climate modeling Seasonal temperature projections RCP 4.5 Western US 



The work was supported in part by 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 and USDA-NIFA Grant 2013-67003-20652, and Sihan Li was funded through a Department of the Interior Northwest Climate Science Center graduate fellowship. The authors thank Andy Bowery, Friederike Otto, and the entire team at Weather@home for their assistance and the thousands of volunteers who supplied computing power for this study, and also thank Richard Betts from the UK Met Office Hadley Centre and William Ingram from the University of Oxford for their expert consultation. Weather@home uses The Berkeley Open Infrastructure for Network Computing (BOINC), which is supported by the National Science Foundation through awards SCI-0221529, SCI-0438443, SCI-0506411, PHY/0555655, and OCI-0721124. The data used for this paper are available by request from the corresponding author.

Supplementary material

382_2016_3200_MOESM1_ESM.pdf (14.2 mb)
Supplementary material 1 (PDF 14552 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • David E. Rupp
    • 1
    Email author
  • Sihan Li
    • 1
  • Philip W. Mote
    • 1
  • Karen M. Shell
    • 2
  • Neil Massey
    • 3
  • Sarah N. Sparrow
    • 4
  • David C. H. Wallom
    • 4
  • Myles R. Allen
    • 3
    • 5
  1. 1.Oregon Climate Change Research Institute, College of Earth, Ocean, and Atmospheric SciencesOregon State UniversityCorvallisUSA
  2. 2.College of Earth, Ocean, and Atmospheric SciencesOregon State UniversityCorvallisUSA
  3. 3.Environmental Change Institute, School of Geography and the EnvironmentUniversity of OxfordOxfordUK
  4. 4.Oxford e-Research CentreUniversity of OxfordOxfordUK
  5. 5.Atmospheric, Oceanic and Planetary Physics, Department of PhysicsUniversity of OxfordOxfordUK

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