Different ways of framing event attribution questions: the example of warm and wet winters in the UK similar to 2015/16

Different ways of framing event attribution questions: the example of warm and wet winters in the... AbstractAttribution analyses of extreme events estimate changes in the likelihood of their occurrence due to human climatic influences by comparing simulations with and without anthropogenic forcings. Classes of events are commonly considered that only share one or more key characteristics with the observed event. Here we test the sensitivity of attribution assessments to such event definition differences, using the warm and wet winter of 2015/16 in the UK as a case study. A large number of simulations from coupled models and an atmospheric model are employed. In the most basic case, warm and wet events are defined relative to climatological temperature and rainfall thresholds. Several other classes of events are investigated, which, in addition to threshold exceedence, also account for the effect of observed sea surface temperature (SST) anomalies, the circulation flow, or modes of variability present during the reference event. Human influence is estimated to increase the likelihood of warm winters in the UK by a factor of 3 or more for events occurring under any atmospheric and oceanic conditions, but also for events with a similar circulation, or oceanic state to 2015/16. The likelihood of wet winters is found to increase by at least a factor of 1.5 in the general case, but results from the atmospheric model, conditioned on observed SST anomalies, are more uncertain, indicating that decreases in the likelihood are also possible. The robustness of attribution assessments based on atmospheric models is highly dependent on the representation of SSTs without the effect of human influence. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Climate American Meteorological Society

Different ways of framing event attribution questions: the example of warm and wet winters in the UK similar to 2015/16

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0442
D.O.I.
10.1175/JCLI-D-17-0464.1
Publisher site
See Article on Publisher Site

Abstract

AbstractAttribution analyses of extreme events estimate changes in the likelihood of their occurrence due to human climatic influences by comparing simulations with and without anthropogenic forcings. Classes of events are commonly considered that only share one or more key characteristics with the observed event. Here we test the sensitivity of attribution assessments to such event definition differences, using the warm and wet winter of 2015/16 in the UK as a case study. A large number of simulations from coupled models and an atmospheric model are employed. In the most basic case, warm and wet events are defined relative to climatological temperature and rainfall thresholds. Several other classes of events are investigated, which, in addition to threshold exceedence, also account for the effect of observed sea surface temperature (SST) anomalies, the circulation flow, or modes of variability present during the reference event. Human influence is estimated to increase the likelihood of warm winters in the UK by a factor of 3 or more for events occurring under any atmospheric and oceanic conditions, but also for events with a similar circulation, or oceanic state to 2015/16. The likelihood of wet winters is found to increase by at least a factor of 1.5 in the general case, but results from the atmospheric model, conditioned on observed SST anomalies, are more uncertain, indicating that decreases in the likelihood are also possible. The robustness of attribution assessments based on atmospheric models is highly dependent on the representation of SSTs without the effect of human influence.

Journal

Journal of ClimateAmerican Meteorological Society

Published: Feb 15, 2018

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