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Title: Exploring precipitation pattern scaling methodologies and robustness among CMIP5 models

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 CO 2 portion and a non-CO 2 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 CO 2-/non-CO 2-forcing decomposition is applied. As a result, 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.
Authors:
ORCiD logo [1] ; ORCiD logo [2] ;  [2] ; ORCiD logo [2]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Pacific Northwest National Lab. (PNNL), College Park, MD (United States)
Publication Date:
Report Number(s):
PNNL-SA-121575
Journal ID: ISSN 1991-9603; KP1703030
Grant/Contract Number:
AC05-76RL01830
Type:
Accepted Manuscript
Journal Name:
Geoscientific Model Development (Online)
Additional Journal Information:
Journal Name: Geoscientific Model Development (Online); Journal Volume: 10; Journal Issue: 5; Journal ID: ISSN 1991-9603
Publisher:
European Geosciences Union
Research Org:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES
OSTI Identifier:
1358485

Kravitz, Ben, Lynch, Cary, Hartin, Corinne, and Bond-Lamberty, Ben. Exploring precipitation pattern scaling methodologies and robustness among CMIP5 models. United States: N. p., Web. doi:10.5194/gmd-10-1889-2017.
Kravitz, Ben, Lynch, Cary, Hartin, Corinne, & Bond-Lamberty, Ben. Exploring precipitation pattern scaling methodologies and robustness among CMIP5 models. United States. doi:10.5194/gmd-10-1889-2017.
Kravitz, Ben, Lynch, Cary, Hartin, Corinne, and Bond-Lamberty, Ben. 2017. "Exploring precipitation pattern scaling methodologies and robustness among CMIP5 models". United States. doi:10.5194/gmd-10-1889-2017. https://www.osti.gov/servlets/purl/1358485.
@article{osti_1358485,
title = {Exploring precipitation pattern scaling methodologies and robustness among CMIP5 models},
author = {Kravitz, Ben and Lynch, Cary and Hartin, Corinne and Bond-Lamberty, Ben},
abstractNote = {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. As a result, 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},
journal = {Geoscientific Model Development (Online)},
number = 5,
volume = 10,
place = {United States},
year = {2017},
month = {5}
}