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An asynchronous regional regression model for statistical downscaling of daily climate variables

Dates

Publication Date

Citation

Stoner, Anne M. K., Hayhoe, Katharine, Yang, Xiaohui, and Wuebbles, Donald J., 2012-08-04, An asynchronous regional regression model for statistical downscaling of daily climate variables: International Journal of Climatology, v. 33, iss. 11.

Summary

The asynchronous regional regression model (ARRM) is a flexible and computationally efficient statistical model that can downscale station-based or gridded daily values of any variable that can be transformed into an approximately symmetric distribution and for which a large-scale predictor exists. This technique was developed to bridge the gap between large-scale outputs from atmosphere–ocean general circulation models (AOGCMs) and the fine-scale output required for local and regional climate impact assessments. ARRM uses piecewise regression to quantify the relationship between observed and modelled quantiles and then downscale future projections. Here, we evaluate the performance of three successive versions of the model in downscaling [...]

Contacts

Communities

  • National and Regional Climate Adaptation Science Centers
  • Southeast CASC

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Created from Item #528d2763e4b0c629af455a4c

Additional Information

Citation Extension

journalInternational Journal of Climatology
parts
typedoi
value10.1002/joc.3603
typestartPage
value2473
typeissn
value1097-0088
typeissue
value11
typeendPage
value2494
typevolume
value33

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