Emulating climate extreme indices

We use simple pattern scaling and time-shift to emulate changes in a set of climate extreme indices under future scenarios, and we evaluate the emulators' accuracy. We propose an error metric that separates systematic emulation errors from discrepancies between emulated and target values due to...

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Bibliographic Details
Published in:	Environmental research letters Vol. 15; no. 7; pp. 74006 - 74015
Main Authors:	Tebaldi, C, Armbruster, A, Engler, H P, Link, R
Format:	Journal Article
Language:	English
Published:	Bristol IOP Publishing 01-07-2020
Subjects:	Climate change Climate models emulation Emulators ENVIRONMENTAL SCIENCES error metric Errors Extreme indices High temperature internal variability pattern scaling scenarios time shift Variability
Online Access:	Get full text
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Summary:	We use simple pattern scaling and time-shift to emulate changes in a set of climate extreme indices under future scenarios, and we evaluate the emulators' accuracy. We propose an error metric that separates systematic emulation errors from discrepancies between emulated and target values due to internal variability, taking advantage of the availability of climate model simulations in the form of initial condition ensembles. We compute the error metric at grid-point scale, and we show geographically resolved results, or aggregate them as global averages. We use a range of scenarios spanning global temperature increases by the end of the century of 1.5 C and 2.0 C compared to a pre-industrial baseline, and two higher trajectories, RCP4.5 and RCP8.5. With this suite of scenarios we can test the effects on the error of the size of the temperature gap between emulation origin and target scenarios. We find that in the emulation of most indices the dominant source of discrepancy is internal variability. For at least one index, however, counting exceedances of a high temperature threshold, significant portions of the globally aggregated discrepancy and its regional pattern originate from the systematic emulation error. The metric also highlights a fundamental difference in the two methods related to the simulation of internal variability, which is significantly resized by simple pattern scaling. This aspect needs to be considered when using these methods in applications where preserving variability for uncertainty quantification is important. We propose our metric as a diagnostic tool, facilitating the formulation of scientific hypotheses on the reasons for the error. In the meantime, we show that for many impact relevant indices these two well established emulation techniques perform accurately when measured against internal variability, establishing the fundamental condition for using them to represent climate drivers in impact modeling.
Bibliography:	ERL-107915.R1 AC05-76RL01830; IA1947282 PNNL-SA-149475 National Science Foundation (NSF) USDOE Office of Science (SC), Biological and Environmental Research (BER)
ISSN:	1748-9326 1748-9326
DOI:	10.1088/1748-9326/ab8332