15 Sep 2022
15 Sep 2022
Status: this preprint is open for discussion.

Understanding pattern scaling errors across a range of emissions pathways

Christopher D. Wells1,2, Lawrence S. Jackson1,2, Amanda C. Maycock1,2, and Piers M. Forster1,2 Christopher D. Wells et al.
  • 1Institute of Climate and Atmospheric Science, University of Leeds, Leeds, UK
  • 2Priestley International Centre for Climate, School of Earth and Environment, University of Leeds, Leeds, UK

Abstract. The regional impacts of multiple possible future emission scenarios can be estimated by combining a few Earth System Model (ESM) simulations with a linear pattern scaling model such as MESMER which uses the pattern of local temperature responses per degree global warming. Here we use MESMER to emulate the future regional pattern of surface temperature response based on historical single-forcer and future Shared Socioeconomic Pathway (SSP) CMIP6 simulations. Pattern scaling errors are decomposed into two components: differences in scaling patterns between scenarios, and intrinsic timeseries differences between local and global responses in the target scenario. The timeseries error is relatively small for high-emissions scenarios, contributing around 20 % of the total error, but is similar in magnitude to the pattern error for lower-emission scenarios. This irreducible timeseries error limits the efficacy of pattern scaling for emulating strong mitigation pathways and reduces the dependence on the predictor pattern used. The results help guide the choice of predictor scenarios and where to target introducing other dependent variables beyond global surface temperature into pattern scaling models.

Christopher D. Wells et al.

Status: open (until 29 Oct 2022)

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Christopher D. Wells et al.

Christopher D. Wells et al.


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Short summary
There are lots of possibilities for future emissions, with different impacts in different places. Complex models can study these impacts, but take a long time to run even on powerful computers. Simple methods can be used to reduce this time by estimating the complex model output, but these aren’t perfect. This study looks at the accuracy of one of these techniques, showing that there are limitations in its use, especially for low emissions future scenarios.