Climate Dynamics

, Volume 40, Issue 3, pp 839–856

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

Authors

    • Scripps Institution of Oceanography, SIO/CASPO
  • Tapash Das
    • Scripps Institution of Oceanography, SIO/CASPO
    • CH2M HILL, Inc.
  • Daniel R. Cayan
    • Scripps Institution of Oceanography, SIO/CASPO
  • Edwin P. Maurer
    • Santa Clara University
  • Norman L. Miller
    • University of California, Berkeley
  • Yan Bao
    • University of California, Berkeley
  • M. Kanamitsu
    • Scripps Institution of Oceanography, SIO/CASPO
  • Kei Yoshimura
    • Scripps Institution of Oceanography, SIO/CASPO
  • Mark A. Snyder
    • University of California, Santa Cruz
  • Lisa C. Sloan
    • University of California, Santa Cruz
  • Guido Franco
    • California Energy Commission
  • Mary Tyree
    • Scripps Institution of Oceanography, SIO/CASPO
Article

DOI: 10.1007/s00382-012-1337-9

Cite this article as:
Pierce, D.W., Das, T., Cayan, D.R. et al. Clim Dyn (2013) 40: 839. doi:10.1007/s00382-012-1337-9

Abstract

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.

Keywords

Climate changeRegional climate modelingDynamical downscalingStatistical downscaling

Supplementary material

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

Copyright information

© Springer-Verlag 2012