Uncertainty component estimates in transient climate projections View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-01-30

AUTHORS

Benoit Hingray, Juliette Blanchet, Guillaume Evin, Jean-Philippe Vidal

ABSTRACT

Quantifying model uncertainty and internal variability components in climate projections has been paid a great attention in the recent years. For multiple synthetic ensembles of climate projections, we compare the precision of uncertainty component estimates obtained respectively with the two Analysis of Variance (ANOVA) approaches mostly used in recent works: the popular Single Time approach (STANOVA), based on the data available for the considered projection lead time and a time series based approach (QEANOVA), which assumes quasi-ergodicity of climate outputs over the available simulation period. We show that the precision of all uncertainty estimates is higher when more members are used, when internal variability is smaller and/or the response-to-uncertainty ratio is higher. QEANOVA estimates are much more precise than STANOVA ones: QEANOVA simulated confidence intervals are roughly 3–5 times smaller than STANOVA ones. Except for STANOVA when less than three members is available, the precision is rather high for total uncertainty and moderate for internal variability estimates. For model uncertainty or response-to-uncertainty ratio estimates, the precision is low for QEANOVA to very low for STANOVA. In the most unfavorable configurations (small number of members, large internal variability), large over- or underestimation of uncertainty components is thus very likely. In a number of cases, the uncertainty analysis should thus be preferentially carried out with a time series approach or with a local-time series approach, applied to all predictions available in the temporal neighborhood of the target prediction lead time. More... »

PAGES

1-16

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00382-019-04635-1

DOI

http://dx.doi.org/10.1007/s00382-019-04635-1

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https://app.dimensions.ai/details/publication/pub.1111779844


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