Climate Dynamics

, Volume 40, Issue 3–4, pp 839–856 | Cite as

Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling

  • David W. PierceEmail author
  • Tapash Das
  • Daniel R. Cayan
  • Edwin P. Maurer
  • Norman L. Miller
  • Yan Bao
  • M. Kanamitsu
  • Kei Yoshimura
  • Mark A. Snyder
  • Lisa C. Sloan
  • Guido Franco
  • Mary Tyree


Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling.


Climate change Regional climate modeling Dynamical downscaling Statistical downscaling 



This work was funded by the public interest energy research (PIER) program of the California Energy Commission (CEC), grant 500-07-042 to the Scripps Institution of Oceanography at UC San Diego: Development of probabilistic climate projections for California. We would also like to thank the global modeling groups that contributed data to the CMIP-3 archive; without their efforts and generosity in sharing the data, this work would have been impossible. DWP also received partial support from the International ad-hoc Detection and Attribution (IDAG) project from the US Department of Energy’s Office of Science, Office of Biological and Environmental Research, grant DE-SC0004956 and the National Oceanic and Atmospheric Administration’s Climate Program Office, and the Department of Energy grant DE-SC0002000 in furtherance of work to examine how daily timescale weather events and the seasonality of precipitation change to accomplish low frequency, global climate changes. Partial salary support for TD from the CALFED Bay-Delta Program funded-postdoctoral fellowship grant is also acknowledged.

Supplementary material

382_2012_1337_MOESM1_ESM.doc (68 kb)
Supplementary material 1 (DOC 68 kb)


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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • David W. Pierce
    • 1
    Email author
  • Tapash Das
    • 1
    • 6
  • Daniel R. Cayan
    • 1
  • Edwin P. Maurer
    • 2
  • Norman L. Miller
    • 3
  • Yan Bao
    • 3
  • M. Kanamitsu
    • 1
  • Kei Yoshimura
    • 1
  • Mark A. Snyder
    • 4
  • Lisa C. Sloan
    • 4
  • Guido Franco
    • 5
  • Mary Tyree
    • 1
  1. 1.Scripps Institution of Oceanography, SIO/CASPOLa JollaUSA
  2. 2.Santa Clara UniversitySanta ClaraUSA
  3. 3.University of California, BerkeleyBerkeleyUSA
  4. 4.University of California, Santa CruzSanta CruzUSA
  5. 5.California Energy CommissionSacramentoUSA
  6. 6.CH2M HILL, Inc.San DiegoUSA

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