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Accounting for skill in trend, variability, and autocorrelation facilitates better multi-model projections: Application to the AMOC and temperature time series

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Title
Accounting for skill in trend, variability, and autocorrelation facilitates better multi-model projections: Application to the AMOC and temperature time series
Author(s)
Roman Olson; An, Soon-Il; Fan, Yanan; Evans, Jason P.
Publication Date
2019-04
Journal
PLOS ONE, v.14, no.4
Publisher
PUBLIC LIBRARY SCIENCE
Abstract
We present a novel quasi-Bayesian method to weight multiple dynamical models by their skill at capturing both potentially non-linear trends and first-order autocorrelated variability of the underlying process, and to make weighted probabilistic projections. We validate the method using a suite of one-at-a-time cross-validation experiments involving Atlantic meridional overturning circulation (AMOC), its temperature-based index, as well as Korean summer mean maximum temperature. In these experiments the method tends to exhibit superior skill over a trend-only Bayesian model averaging weighting method in terms of weight assignment and probabilistic forecasts. Specifically, mean credible interval width, and mean absolute error of the projections tend to improve. We apply the method to a problem of projecting summer mean maximum temperature change over Korea by the end of the 21 st century using a multi-model ensemble. Compared to the trend-only method, the new method appreciably sharpens the probability distribution function (pdf) and increases future most likely, median, and mean warming in Korea. The method is flexible, with a potential to improve forecasts in geosciences and other fields.
URI
https://pr.ibs.re.kr/handle/8788114/10589
DOI
10.1371/journal.pone.0214535
ISSN
1932-6203
Appears in Collections:
Center for Climate Physics(기후물리 연구단) > 1. Journal Papers (저널논문)
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