Exploring Precipitation Pattern Scaling Methodologies and Robustness Among CMIP5 Models

TitleExploring Precipitation Pattern Scaling Methodologies and Robustness Among CMIP5 Models
Publication TypeJournal Article
Year of Publication2017
AuthorsKravitz, Ben, Lynch Cary, Hartin Corinne, and Bond-Lamberty Ben
JournalGeoscientific Model Development
Volume10
Pages1889-1902
Date Published05/2017
Abstract / Summary

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 two methods of pattern scaling for annual mean precipitation (regression and epoch difference) and evaluate which method is better in particular circumstances by quantifying their robustness to interpolation/extrapolation in time, inter-model variations, and inter-scenario variations. Both the regression and epoch-difference methods (the two most commonly used methods of pattern scaling) have good absolute performance in reconstructing the climate model output, measured as an area-weighted root mean square error. We decompose the precipitation response in the RCP8.5 scenario into a CO2 portion and a non-CO2 portion. Extrapolating RCP8.5 patterns to reconstruct precipitation change in the RCP2.6 scenario results in large errors due to violations of pattern scaling assumptions when this CO2-/non-CO2-forcing decomposition is applied. The methodologies discussed in this paper can help provide precipitation fields to be utilized in other models (including integrated assessment models or impacts assessment models) for a wide variety of scenarios of future climate change.

URLhttps://www.geosci-model-dev.net/10/1889/2017/
DOI10.5194/gmd-10-1889-2017
Funding Program: 
Journal: Geoscientific Model Development
Year of Publication: 2017
Volume: 10
Pages: 1889-1902
Date Published: 05/2017

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 two methods of pattern scaling for annual mean precipitation (regression and epoch difference) and evaluate which method is better in particular circumstances by quantifying their robustness to interpolation/extrapolation in time, inter-model variations, and inter-scenario variations. Both the regression and epoch-difference methods (the two most commonly used methods of pattern scaling) have good absolute performance in reconstructing the climate model output, measured as an area-weighted root mean square error. We decompose the precipitation response in the RCP8.5 scenario into a CO2 portion and a non-CO2 portion. Extrapolating RCP8.5 patterns to reconstruct precipitation change in the RCP2.6 scenario results in large errors due to violations of pattern scaling assumptions when this CO2-/non-CO2-forcing decomposition is applied. The methodologies discussed in this paper can help provide precipitation fields to be utilized in other models (including integrated assessment models or impacts assessment models) for a wide variety of scenarios of future climate change.

DOI: 10.5194/gmd-10-1889-2017
Citation:
Kravitz, B, C Lynch, C Hartin, and B Bond-Lamberty.  2017.  "Exploring Precipitation Pattern Scaling Methodologies and Robustness Among CMIP5 Models."  Geoscientific Model Development 10: 1889-1902.  https://doi.org/10.5194/gmd-10-1889-2017.