Exploring precipitation pattern scaling methodologies and robustness among CMIP5 models
Ben Kravitz1, Cary Lynch2, Corinne Hartin2, and Ben Bond-Lamberty21Atmospheric 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
Received: 05 Oct 2016 – Accepted for review: 22 Nov 2016 – Discussion started: 23 Nov 2016
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.
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.