The influence of model resolution on temperature variability
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Understanding future changes in climate variability, which can impact human activities, is a current research priority. It is often assumed that a key part of this effort involves improving the spatial resolution of climate models; however, few previous studies comprehensively evaluate the effects of model resolution on variability. In this study, we systematically examine the sensitivity of temperature variability to horizontal atmospheric resolution in a single model (CCSM3, the Community Climate System Model 3) at three different resolutions (T85, T42, and T31), using spectral analysis to describe the frequency dependence of differences. We find that in these runs, increased model resolution is associated with reduced temperature variability at all but the highest frequencies (2–5 day periods), though with strong regional differences. (In the tropics, where temperature fluctuations are smallest, increased resolution is associated with increased variability.) At all resolutions, temperature fluctuations in CCSM3 are highly spatially correlated, implying that the changes in variability with model resolution are driven by alterations in large-scale phenomena. Because CCSM3 generally overestimates temperature variability relative to reanalysis output, the reductions in variability associated with increased resolution tend to improve model fidelity. However, the resolution-related variability differences are relatively uniform with frequency, whereas the sign of model bias changes at interannual frequencies. This discrepancy raises questions about the mechanisms underlying the improvement at subannual frequencies. The consistent response across frequencies also implies that the atmosphere plays a significant role in interannual variability.
KeywordsClimate variability Variability Model resolution CCSM3 Spectral analysis
We thank Matthew Huber, Robert Jacob, Ben Kirtman, Leonard Smith, and Michael Stein for helpful comments on this paper. This research was performed as part of the Center for Robust Decision-making on Climate and Energy Policy (RDCEP) at the University of Chicago, funded by a grant from the National Science Foundation (NSF) Decision Making Under Uncertainty program (SES-0951576). Model runs were completed by NCAR and are available publicly on www.earthsystemgrid.com. This work was completed in part with resources provided by the University of Chicago Research Computing Center.
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