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Climatic Change

, Volume 122, Issue 3, pp 459–471 | Cite as

Pattern scaling: Its strengths and limitations, and an update on the latest model simulations

  • Claudia Tebaldi
  • Julie M. Arblaster
Article

Abstract

We review the ideas behind the pattern scaling technique, and focus on its value and limitations given its use for impact assessment and within integrated assessment models. We present estimates of patterns for temperature and precipitation change from the latest transient simulations available from the Coupled Model Inter-comparison Project Phase 5 (CMIP5), focusing on multi-model mean patterns, and characterizing the sources of variability of these patterns across models and scenarios. The patterns are compared to those obtained from the previous set of experiments, under CMIP3. We estimate the significance of the emerging differences between CMIP3 and CMIP5 results through a bootstrap exercise, while also taking into account the fundamental differences in scenario and model ensemble composition. All in all, the robustness of the geographical features in patterns of temperature and precipitation, when computed as multi-model means, is confirmed by this comparison. The intensity of the change (in both the warmer and cooler areas with respect to global temperature change, and the drier and wetter regions) is overall heightened per degree of global warming in the ensemble mean of the new simulations. The presence of stabilized scenarios in the new set of simulations allows investigation of the performance of the technique once the system has gotten close to equilibrium. Overall, the well established validity of the technique in approximating the forced signal of change under increasing concentrations of greenhouse gases is confirmed.

Keywords

Atlantic Meridional Overturning Circulation Precipitation Change Radiative Forcings Internal Variability Multimodel Ensemble 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We thank the editors, Dr. Tom Wigley, Dr. Reto Knutti and two anonymous reviewers for their comments and suggestions.

We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison, and the Working Group on Coupled Modeling of the World Climate Research Programme (WCRP) for their roles in making available the WCRP CMIP3 and CMIP5 multimodel datasets. Support of these datasets is provided by the Office of Science, US Department of Energy (DOE). Portions of this study were supported by the Office of Science, Biological, and Environmental Research, US DOE (Grant DE-SC0004956 and Cooperative Agreement No. DE-FC0297ER62402). The National Center for Atmospheric Research is funded by the National Science Foundation.

Supplementary material

10584_2013_1032_MOESM1_ESM.docx (3 mb)
ESM 1 (DOCX 3083 kb)

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Climate and Global Dynamics, National Center for Atmospheric Research (NCAR)BoulderUSA
  2. 2.NCAR and Bureau of MeteorologyMelbourneAustralia

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