Climate Dynamics

, Volume 36, Issue 7–8, pp 1303–1319 | Cite as

Influence of SST biases on future climate change projections

  • Moetasim AshfaqEmail author
  • Christopher B. Skinner
  • Noah S. Diffenbaugh


We use a quantile-based bias correction technique and a multi-member ensemble of the atmospheric component of NCAR CCSM3 (CAM3) simulations to investigate the influence of sea surface temperature (SST) biases on future climate change projections. The simulations, which cover 1977–1999 in the historical period and 2077–2099 in the future (A1B) period, use the CCSM3-generated SSTs as prescribed boundary conditions. Bias correction is applied to the monthly time-series of SSTs so that the simulated changes in SST mean and variability are preserved. Our comparison of CAM3 simulations with and without SST correction shows that the SST biases affect the precipitation distribution in CAM3 over many regions by introducing errors in atmospheric moisture content and upper-level (lower-level) divergence (convergence). Also, bias correction leads to significantly different precipitation and surface temperature changes over many oceanic and terrestrial regions (predominantly in the tropics) in response to the future anthropogenic increases in greenhouse forcing. The differences in the precipitation response from SST bias correction occur both in the mean and the percent change, and are independent of the ocean–atmosphere coupling. Many of these differences are comparable to or larger than the spread of future precipitation changes across the CMIP3 ensemble. Such biases can affect the simulated terrestrial feedbacks and thermohaline circulations in coupled climate model integrations through changes in the hydrological cycle and ocean salinity. Moreover, biases in CCSM3-generated SSTs are generally similar to the biases in CMIP3 ensemble mean SSTs, suggesting that other GCMs may display a similar sensitivity of projected climate change to SST errors. These results help to quantify the influence of climate model biases on the simulated climate change, and therefore should inform the effort to further develop approaches for reliable climate change projection.


Climate change Sea surface temperature Global climate modeling 



We thank two anonymous reviewers for their constructive and insightful comments. This work was supported in part by NSF award 0450221, DOE awards DE-FG02-08ER64649 and DE-SC0001483, and by the World Bank’s Trust Fund for Environmentally and Socially Sustainable Development. The CAM3 simulations and analyses were enabled by computational resources provided by Information Technology at Purdue (the Rosen Center for Advanced Computing, West Lafayette, Indiana). We thank the CCSM Climate Change Working group at NCAR for access to the CCSM3 simulations. NCEP Reanalysis data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at This is PCCRC paper number 0922.


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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Moetasim Ashfaq
    • 1
    • 2
    • 3
    Email author
  • Christopher B. Skinner
    • 1
    • 2
  • Noah S. Diffenbaugh
    • 1
    • 2
    • 4
  1. 1.Department of Environmental Earth System ScienceStanford UniversityStanfordUSA
  2. 2.Department of Earth and Atmospheric SciencesPurdue UniversityWest LafayetteUSA
  3. 3.Climate Change Science Institute, Oak Ridge National LaboratoryOak RidgeUSA
  4. 4.Woods Institute for the EnvironmentStanford UniversityStanfordUSA

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