Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
doi:10.5194/gmd-2016-258
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Methods for assessment of models
23 Nov 2016
Review status
A revision of this discussion paper was accepted for the journal Geoscientific Model Development (GMD) and is expected to appear here in due course.
Exploring precipitation pattern scaling methodologies and robustness among CMIP5 models
Ben Kravitz1, Cary Lynch2, Corinne Hartin2, and Ben Bond-Lamberty2 1Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
2Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, USA
Abstract. Pattern scaling is a well established method for approximating modeled spatial distributions of changes in temperature by assuming a time-invariant pattern that scales with changes in global mean temperature. We compare three methods of pattern scaling for precipitation (regression, epoch difference, and a physically-based method) and evaluate which methods are “better” in particular circumstances by quantifying their robustness to interpolation/extrapolation, inter-model variations, and inter-scenario variations. Although the regression and epoch difference methods (the two most commonly used methods of pattern scaling) have better absolute performance in reconstructing the climate model output by two orders of magnitude (measured as an area-weighted root mean square error), the physically-based method shows a greater degree of robustness (less relative root-mean-square variation than the other two methods) and could be a particularly advantageous method if outstanding biases could be reduced. We decompose the precipitation response in the RCP8.5 scenario into a CO2 portion and a non-CO2 portion; these two patterns oppose each other in sign. Due to low signal-to-noise ratios, extrapolating RCP8.5 patterns to re- construct precipitation change in the RCP2.6 scenario results in double the error of reconstructing the RCP8.5 scenario for the regression and epoch difference methods. The methodologies discussed in this paper can help provide precipitation fields for other models (including integrated assessment models or impacts assessment models) for a wide variety of scenarios of future climate change.

Citation: Kravitz, B., Lynch, C., Hartin, C., and Bond-Lamberty, B.: Exploring precipitation pattern scaling methodologies and robustness among CMIP5 models, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-258, in review, 2016.
Ben Kravitz et al.
Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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RC1: 'Pattern scaling', Anonymous Referee #1, 16 Dec 2016 Printer-friendly Version Supplement 
AC1: 'Response to reviewer #1', Ben Kravitz, 16 Feb 2017 Printer-friendly Version Supplement 
 
RC2: 'Review for Kravitz et al', Anonymous Referee #2, 14 Jan 2017 Printer-friendly Version 
AC2: 'Response to reviewer #2', Ben Kravitz, 16 Feb 2017 Printer-friendly Version Supplement 
Ben Kravitz et al.
Ben Kravitz et al.

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Short summary
Pattern scaling is a way of approximating regional changes without needing to run a full, complex global climate model. We compare three methods of pattern scaling for precipitation and evaluate which methods are “better”. We also decompose precipitation into a CO2 portion and a non-CO2 portion; these two patterns oppose each other in sign. The methodologies discussed in this paper can help provide precipitation fields for other models for a wide variety of scenarios of future climate change.
Pattern scaling is a way of approximating regional changes without needing to run a full,...
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