Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.252 IF 4.252
  • IF 5-year value: 4.890 IF 5-year 4.890
  • CiteScore value: 4.49 CiteScore 4.49
  • SNIP value: 1.539 SNIP 1.539
  • SJR value: 2.404 SJR 2.404
  • IPP value: 4.28 IPP 4.28
  • h5-index value: 40 h5-index 40
  • Scimago H index value: 51 Scimago H index 51
Volume 10, issue 5 | Copyright
Geosci. Model Dev., 10, 1889-1902, 2017
https://doi.org/10.5194/gmd-10-1889-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Methods for assessment of models 12 May 2017

Methods for assessment of models | 12 May 2017

Exploring precipitation pattern scaling methodologies and robustness among CMIP5 models

Ben Kravitz1, Cary Lynch2, Corinne Hartin2, and Ben Bond-Lamberty2 Ben Kravitz et al.
  • 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 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.

Publications Copernicus
Download
Short summary
Pattern scaling is a way of approximating regional changes without needing to run a full, complex global climate model. We compare two methods of pattern scaling for precipitation and evaluate which methods is better in particular circumstances. We also decompose precipitation into a CO2 portion and a non-CO2 portion. 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,...
Citation
Share