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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 6
Hydrol. Earth Syst. Sci., 17, 2147–2159, 2013
https://doi.org/10.5194/hess-17-2147-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 17, 2147–2159, 2013
https://doi.org/10.5194/hess-17-2147-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 07 Jun 2013

Research article | 07 Jun 2013

Errors in climate model daily precipitation and temperature output: time invariance and implications for bias correction

E. P. Maurer1, T. Das2, and D. R. Cayan3 E. P. Maurer et al.
  • 1Civil Engineering Dept., Santa Clara University, Santa Clara, CA, USA
  • 2CH2MHill, 402 W. Broadway, San Diego, CA, USA
  • 3Division of Climate, Atmospheric Sciences, and Physical Oceanography, Scripps Institution of Oceanography and Water Resources Division, US Geological Survey, La Jolla, CA, USA

Abstract. When correcting for biases in general circulation model (GCM) output, for example when statistically downscaling for regional and local impacts studies, a common assumption is that the GCM biases can be characterized by comparing model simulations and observations for a historical period. We demonstrate some complications in this assumption, with GCM biases varying between mean and extreme values and for different sets of historical years. Daily precipitation and maximum and minimum temperature from late 20th century simulations by four GCMs over the United States were compared to gridded observations. Using random years from the historical record we select a "base" set and a 10 yr independent "projected" set. We compare differences in biases between these sets at median and extreme percentiles. On average a base set with as few as 4 randomly-selected years is often adequate to characterize the biases in daily GCM precipitation and temperature, at both median and extreme values; 12 yr provided higher confidence that bias correction would be successful. This suggests that some of the GCM bias is time invariant. When characterizing bias with a set of consecutive years, the set must be long enough to accommodate regional low frequency variability, since the bias also exhibits this variability. Newer climate models included in the Intergovernmental Panel on Climate Change fifth assessment will allow extending this study for a longer observational period and to finer scales.

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