Validation of climate model output using Bayesian statistical methods |
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| Authors: | Mark A Snyder Bruno Sansó Lisa C Sloan |
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| Institution: | (1) Climate Change and Impacts Laboratory, Department of Earth Sciences, University of California Santa Cruz, Santa Cruz, CA, USA;(2) Department of Applied Mathematics and Statistics, University of California Santa Cruz, Santa Cruz, CA, USA |
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| Abstract: | The growing interest in and emphasis on high spatial resolution estimates of future climate has demonstrated the need to apply
regional climate models (RCMs) to that problem. As a consequence, the need for validation of these models, an assessment of
how well an RCM reproduces a known climate, has also grown. Validation is often performed by comparing RCM output to gridded
climate datasets and/or station data. The primary disadvantage of using gridded climate datasets is that the spatial resolution
is almost always different and generally coarser than climate model output. We have used a Bayesian statistical model derived
from observational data to validate RCM output. We used surface air temperature (SAT) data from 109 observational stations
in California, all with records of approximately 50 years in length, and created a statistical model based on this data. The
statistical model takes into account the elevation of the station, distance from coastline, and the NOAA climate region in
which the station resides. Analysis indicates that the statistical model provides reliable estimates of the mean monthly SAT
at any given station. In our method, the uncertainty in the estimates produced by the statistical model are directly determined
by obtaining probability density functions for predicted SATs. This statistical model is then used to estimate average SATs
corresponding to each of the climate model grid cells. These estimates are compared to the output of the RCM to assess how
well the RCM matches the observed climate as defined by the statistical model. Overall, the match between the RCM output and
the statistical model is good, with some deficiencies likely due in part to the representation of topography in the RCM. |
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