Climatic Change

, Volume 122, Issue 4, pp 609–619 | Cite as

Projected climate change scenario over California by a regional ocean–atmosphere coupled model system

  • Haiqin LiEmail author
  • Masao Kanamitsu
  • Song-You Hong
  • Kei Yoshimura
  • Daniel R. Cayan
  • Vasubandhu Misra
  • Liqiang Sun


This study examines a future climate change scenario over California in a 10-km coupled regional downscaling system of the Regional Spectral Model for the atmosphere and the Regional Ocean Modeling System for the ocean forced by the global Community Climate System Model version 3.0 (CCSM3). In summer, the coupled and uncoupled downscaled experiments capture the warming trend of surface air temperature, consistent with the driving CCSM3 forcing. However, the surface warming change along the California coast is weaker in the coupled downscaled experiment than it is in the uncoupled downscaling. Atmospheric cooling due to upwelling along the coast commonly appears in both the present and future climates, but the effect of upwelling is not fully compensated for by the projected large-scale warming in the coupled downscaling experiment. The projected change of extreme warm events is quite different between the coupled and uncoupled downscaling experiments, with the former projecting a more moderate change. The projected future change in precipitation is not significantly different between coupled and uncoupled downscaling. Both the coupled and uncoupled downscaling integrations predict increased onshore sea breeze change in summer daytime and reduced offshore land breeze change in summer nighttime along the coast from the Bay area to Point Conception. Compared to the simulation of present climate, the coupled and uncoupled downscaling experiments predict 17.5 % and 27.5 % fewer Catalina eddy hours in future climate respectively.


Future Climate Present Climate Community Climate System Model Land Breeze Mesoscale Circulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by grants from National Oceanic and Atmospheric Administration (ECPC: NA17RJ1231, NA12OAR4310078, NA10OAR4310215, NA11OAR4310110), the California Energy Commission PIER Program, U.S. Geological Survey (06HQGR0125), and U.S. Department of Agriculture (027865). The views expressed herein are those of the authors and do not necessarily reflect the views of funding agencies. Supercomputing resources were provided by the Center for Observations, Modeling and Prediction at Scripps Institution of Oceanography and TACC via Extreme Science and Engineering Discovery Environment. The help of Kathy Fearon in refining our text is appreciated. Three anonymous reviewers helped to improve the manuscript.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Haiqin Li
    • 1
    Email author
  • Masao Kanamitsu
    • 2
  • Song-You Hong
    • 3
  • Kei Yoshimura
    • 4
  • Daniel R. Cayan
    • 2
  • Vasubandhu Misra
    • 1
  • Liqiang Sun
    • 5
  1. 1.Center for Ocean–Atmospheric Prediction StudiesFlorida State UniversityTallahasseeUSA
  2. 2.Scripps Institution of OceanographyUniversity of CaliforniaSan DiegoUSA
  3. 3.Department of Atmospheric Sciences, College of ScienceYonsei UniversitySeoulKorea
  4. 4.Atmosphere and Ocean Research InstituteUniversity of TokyoKashiwaJapan
  5. 5.Cooperative Institute for Climate and Satellites – North CarolinaAshevilleUSA

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